We propose an automatic 3D method for the determination of legal maturity from MR images based on the ossification of the radius bone . Age estimation is performed by a linear regression model of the epiphyseal gap volume over the known ground truth age of training data . Results are comparable with the established Greulich/Pyle ( GP ) and Tanner/Whitehouse ( TW ) methods , but do not involve harmful radiation . Linear canonical correlation analysis ( CCA ) has been widely applied owing to low complexity , small and fixed amount of model parameters and good scalability . However , linear CCA based regression gets lower accuracy than its kernel version on the age estimation problem . The inexactness of metric distance information carried by age labels increases the complexity of using regression-based methods to estimate age . Furthermore , a comparative study and critical analysis of the existing and recent algorithms is conducted . Hence the research gaps are identified for future work in age estimation problem . Four different subsets of the MORPH-II database were selected for a wide range of purposes , including age estimate , gender and race classification , and facial recognition . Due to the variety of poses and lighting conditions present in the MORPH-II database , certain preprocessing techniques were used to standardize the images and make them more suitable for machine learning tasks . However , they have their own characteristics in the algorithm by adopting diverse approaches to optimize the regression such as deep learning , Gaussian process , local binary features and so nonexperimental results show all of them can achieve improvements compare to state-of-art methods . With facial point detection , information of human face can be utilized for facial analysis applications , such as face recognition , face synthesis and age estimate . This paper presents a method to estimate age from facial images . Firstly , we extract the facial features for age estimating automatically using AAM , then classify these facial features by different ages . In order to improve the precision of the classifying , we present a method which is Artificial Immune Recognition System based on Partial Hausdorff distance , describe the process of how to train the system and how to classify the facial features . To show the effectiveness of proposed method , we first compare Relative Tree method with Relative Attribute on PubFig and OSR dataset . Then to verify the efficiency of Relative Forest algorithm , we conduct age estimation evaluation on FG-NET dataset . With much less training time compared to Relative Attribute and Relative Tree , proposed Relative Forest achieves state-of-the-art age estimation accuracy . Age Estimation Components , i.e. the extraction and localization of facial features landmark points , which represent ageing pattern are very important steps in the age estimation process . A good age estimation technique will contain enough variation of landmark points and consider all important points to express the full complexity of the problem . Different classification methods are proposed for automatic human age estimation in Malaysian population . This paper provide significant implications in the computation of fragmentary skeletal remains and forensic population samples for age estimation purpose . Here , this problem is addressed by making the following contributions . First , in answer to one of the key problems of age estimation research — absence of data — a unique data set of face images , labelled for age and gender is offered , acquired by smart-phones and other mobile devices , and uploaded without manual filtering to online image repositories . The images in this collection are more challenging than those offered by other face-photo benchmarks . Age estimation is an important method to solve the face recognition with age change , due to the feature extraction , in the process of age estimation study , PCA dimensional reduction method is usually used to reduce dimension with excessive dimension . PCA refers that transform the sample matrix into one-dimensional vector first , then the one-dimensional vectors form a matrix , solve the eigenvector . In comparison with metric regression , which assumes that is a continuous random variable , ordinal regression regards as a finite sequence where the metric distance between ranks is not defined . Along with age estimation niu2016ordinal , popular applications for ordinal regression include predicting the progression of various diseases , such as Alzheimer ’s disease doyle2014predicting , Crohn ’s disease , artery disease , and kidney disease . Also , ordinal regression models are common choices for text message advertising and various recommender systems . Our proposed method addresses both of these issues with a theoretical guarantee for classifier consistency as well as a reduction of the training complexity . subsection : CNN Architectures for Age Estimation Due to its broad utility in social networking , video surveillance , and biometric verification , age estimation from human faces is an area of active research . It does this by using an R - CNN based approach and a modified AlexNet architecture which fuses intermediate convolutional layer outputs and adds separate fully - connected networks to predict each subtask . All - In - One Convolutional Neural Network for Face Analysis adds smile , age estimation and facial recognition to the former prediction tasks . We compare our results to all of these works . Face hallucination refers to obtaining a clean face image from a degraded ones . The degraded face is assumed to be related to the clean face through the forward imaging model that account for blurring , sampling and noise . Learning-based SR algorithms infer latent HR images by the guidance of coexisted priors from training samples . Various regularization methods have been successfully applied in face hallucination to ameliorate its ill-posed nature . But most of them only consider the local manifold geometry of a single patch which results in an unstable solution for SR reconstruction . The utilization of this dependency relation together with component priors would regularize the solution more . In this work a fast synthesis based approach is proposed for the face hallucination problem by incorporating shape information , texture information and the statistical dependency of the image components simultaneously . Experiments show that more accurate reconstructions can be obtained with less computational effort . In this paper , we propose a novel learning-based face hallucination framework built in DCT domain , which can recover the high-resolution face image from a single low-resolution one . Unlike most previous learning-based work , our approach addresses the face hallucination problem from a different angle . In details , the problem is formulated as inferring DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain . In this paper , we propose a region-based eigentransformation method for human face hallucination . The algorithm first uses the snake method to locate the targeted regions with high-frequency features in an edge-smoothed face mask , and then employs the eigentransformation to reconstruct the corresponding high-resolution counterpart . On the basis of this finding , we suggest a stepwise reconstruction scheme to minimize inconsistency risk in solution space . In particular , this scheme divides face hallucination process into multiple cascaded incremental training-synthesis steps , in which each individual step allows smaller magnifying factor as well as the corresponding intermediate resolution ( IR ) dictionary rather than merely LR and HR dictionary based learning . Moreover , in order to keep sparse representation ( SR ) sufficiently sparse while favoring its locality , we introduce a weighted ℓ1/ℓ2 mixed norms minimization SR method and formulate a unified framework together with stepwise scheme . Additionally , they ignore the influence of spatial distances between the test image and training basis images on optimal reconstruction coefficients . Consequently , they can not offer a satisfactory performance in practical face hallucination applications . In this paper , we propose a weighted adaptive sparse regularization ( WASR ) method to promote accuracy , stability and robustness for face hallucination reconstruction , in which a distance-inducing weighted ℓq norm penalty is imposed on the solution . With the adjustment to shrinkage parameter q , the weighted ℓq penalty function enables elastic description ability in the sparse domain , leading to more conservative sparsity in an ascending order of q . Therefore , it is difficult to generate facial attributes accurately . In this paper , we treat the sketch to face the problem as a face hallucination reconstruction problem . In order to solve this problem , we propose an image translation network by exploiting attributes with the generated adversarial network . Subsequently , we formulate a Bayesian approach accounting for the statistical inference from sketches to their corresponding photos in terms of the learned tensor model . Comparative experiments are conducted to contrast the proposed method with state-of-the-art algorithms for facial sketch synthesis in a novel face hallucination scenario : sketch-based facial photo hallucination . The encouraging results obtained convincingly validate the effectiveness of our method . For recognition purpose , we prefer to obtain near-frontal faces . To this end , we propose a nuclear norm regularized structural orthogonal Procrustes regression ( N2SOPR ) approach in this work to acquire pose-robust feature representations for face hallucination with pose . The orthogonal Procrustes regression is used to seek an appropriate transformation between two data matrixes . In addition , in the residual-compensation process , a linear-mapping method is proposed to include both the inter- and intrainformation about manifolds of different resolutions . Compared with other state-of-the-art approaches , the proposed framework can achieve a comparable , or even better , performance in terms of global face reconstruction and the visual quality of face hallucination . Experiments on images with various parameter settings and blurring distortions show that the proposed approach is robust and has great potential for real-world applications . [ reference ] . The external example - based methods can be formulated for generic image super - resolution , or can be designed to suit domain specific tasks , i.e. , face hallucination [ reference ] , [ reference ] , according to the training samples provided . The sparse - coding - based method [ reference ] , [ reference ] is one of the representative external example - based SR methods . Furthermore , a denoising network is presented to serve for perceptual regularization to suppress the artifacts in transfer result , while an attribute ratio regularization is introduced to constrain the size of attribute relevant region . Our DIAT can provide a unified solution for several representative facial attribute transfer tasks , e . g . , expression transfer , accessory removal , age progression and gender transfer , and can be extended for other face enhancement tasks such as face hallucination . The experimental results validate the effectiveness of the proposed method . Here we use the face hallucination as an example . Face hallucination is undoubtedly a global transfer task , and thus we remove the mask network as well as the attribute ratio regularization , making . Moreover , the input image in face hallucination is of low resolution ( LR ) while the output image is of high resolution ( HR ) . For example , it is able to predict the gender . Recently , the gender prediction based on handwriting becomes an interesting research . Even in 2013 , an competition for prediction gender from handwriting has been held by Kaggle . The proposed method predicts race and gender from a single eye image with an accuracy of 86 % and 90 % , respectively . In addition , the following analysis are conducted : ( a ) the role of different parts of the ocular region on attribute prediction ; ( b ) the influence of gender on race prediction , and vice-versa ; ( c ) the impact of eye color on gender and race prediction ; ( d ) the impact of image blur on gender and race prediction ; ( e ) the generalizability of the method across different datasets , i.e. , cross-dataset performance ; and ( f ) the consistency of prediction performance across the left and right eyes . The model applied by using big data technology and achieved 85.6 % accuracy in terms of user gender prediction and 65.5 % of user age prediction . The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain . We then show that this knowledge can be used in the analysis of first-person communication ; knowledge of distinguishing attributes allows us to both classify users and to bootstrap new training examples . Our novel approach enables substantial improvements on the widelystudied task of user gender prediction , obtaining a 20 % relative error reduction over the current state-of-the-art . The CLIN 29 shared task is concerned with binary gender prediction within and across different genres in Dutch . Our proposed approaches to this problem are a simple model , which uses character ngrams , and a more complex model which consists of two systems in a co-training setup . The first one ( VGG16/10 ) considers gender and age as two related characteristics and the final neurons have been conceived to hold these aspects together at the same time . The second one ( VGG16/8 + 1 ) in the prediction layer has got a neuron for the prediction of gender and another eight for the prediction of age ( according to the Adience benchmark ) . Such networks have been conceived to provide simultaneously information on both the gender and age of the person identified in the image , without the need to build two dedicated networks . Apart from human recognition , gait has lately become a promising biometric feature also useful for prediction of gender . One of the most popular methods to represent gait is the well-known Gait Energy Image ( GEI ) , which conducts to a high-dimensional Euclidean space where many features are irrelevant . The task of predicting demographics of social media users , bloggers and authors of other types of online texts is crucial for marketing , security , etc . However , most of the papers in authorship profiling deal with author gender prediction . In addition , most of the studies are performed in English-language corpora and very little work in the area in the Russian language . This paper introduces an empirical investigation which directly addresses and explores gender prediction capacity from digitised handwriting data from several different perspectives - such as feature type ( static/dynamic ) and content ( fixed/variable ) types - in order to provide extensive experimental evidence and analysis to guide the development of a better understanding of the opportunities for and practical consequences of gender prediction from digital handwriting data . Term popularity and discriminability is important for on-device gender prediction . With the proliferation of mobile devices and the growing necessity for gender information in personalized intelligent systems , gender prediction of mobile users has become an important research issue . Text data in mobile devices are known to have high discriminative power for gender , but transmitting those data to the outside of a device has a security risk and raises a privacy concern of users . The text data consists of articles that the users have read that are representative of the minority class . Our goal is to improve the predictive power of supervised learning algorithms for the gender prediction problem , using articles consumed by the user from a large health-based website as our data source . Our study shows that by creating synthetic user profiles for the minority class with SeqGANs and passing in the resampled training data to an XGBoost classifier , we achieve a gain of 2 % in AUROC , as well as a 3 % gain in both F1-Score and AUPR for gender prediction when compared to SMOTE . Age distribution learning has made other notable progress in age estimation ; here , the researchers defined a new loss function to penalize the difference between estimated age distributions and the ground truth age labels . Recent research has also shown that training a multi - task CNN for various face analysis tasks , including face detection , gender prediction , age estimation , etc . , can improve the overall performance across different tasks compared to a single - task CNN ranjan2017all by sharing lower - layer parameters . In chen2016cascaded , a cascaded convolutional neural network was designed to classify face images into age groups followed by regression modules for more accurate age estimation . Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose , speech , facial expression , behaviour and actions which are used in conveying information about the intentions and emotions of a human being . In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis ( PCA ) is used for dimensionality reduction . In this paper , we propose a null space discriminant locality preserving projections ( NDLPP ) method for facial feature extraction and recognition . Based on locality preserving projections ( LPP ) and discriminant locality preserving projections ( DLPP ) methods , NDLPP comes into the characteristics of DLPP that encodes both the geometrical and discriminant structure of the data manifold , and addresses the small sample size problem by solving an eigenvalue problem in null space . Facial recognition and verification is a widely used biometric technology in security system . Unfortunately , face biometrics is vulnerable to spoofing attacks using photographs or videos . Facial landmark localization is important to many facial recognition and analysis tasks , such as face attributes analysis , head pose estimation , 3D face modeling , and facial expression analysis . In this paper , we propose a new approach to localizing landmarks in facial image by deep convolutional neural network ( DCNN ) . Facial landmark localization plays a critical role in facial recognition and analysis . In this chapter , we first propose a novel cascaded backbone-branches fully convolutional neural network ( BB-FCN ) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings . Distributed Simulation of Modular Neural Networks.- Dynamical Behavior of an Electronic Neuron of Commutation.- Training Neural Networks Using Non-standard Norms -Knowledge Representation and Reasoning.- Searching for a Solution to Program Verification = Equation Solving in CCS.- Knowledge Representation Using High-Level Non-monotonic Reasoning.- Experiments on Information Retrieval Using Case-Based Reasoning.- A Probabilistic Exemplar-Based Model for Case-Based Reasoning.- Intensification and Diversification Strategies with Tabu Search : One-Machine Problem with Weighted Tardiness Objective.- A Methodology to Parallel the Temperature Cycle in Simulated Annealing.- Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using the Threshold Accepting Algorithm.- A Practical Approach for Logic Program Analysis and Transformation.- Experiments in Answer Sets Planning.- Robotics and Computer Vision.- Competitive Learning Methods for Efficient Vector Quantizations in a Speech Recognition Environment.- Facial Expression Recognition and Modeling for Virtual Intelligent Tutoring Systems.- Detection and Tracking of Facial Features in Video Sequences.- An Application of Behavior-Based Architecture for Mobile Robots Design.- Cooperative Simulated Annealing for Path Planning in Multi-robot Systems.- Learning Probabilistic Grid-Based Maps for Indoor Mobile Robots Using Ultrasonic and Laser Range Sensors.- Evolving Insect Locomotion Using Cooperative Genetic Programming.- On the Minimal Cooperative Attitude in Multi-robotics Systems.- Eigenfaces Versus Eigeneyes : Facial analysis and recognition is one of the most popular information processing tasks . Face recognition requires some form of dimensionality reduction in the face dataset . To overcome these limitations and take full advantage of the spatio-temporal information , we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events . We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition . Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles , as well as its advantage over the existing methods . This paper presents a method for facial feature representation and recognition from the Covariance Matrices of the Gabor-filtered images . Gabor filters are a very powerful tool for processing images that respond to different local orientations and wave numbers around points of interest , especially on the local features on the face . Driver fatigue can also be categorized under driver behavioral as it comes from the driver itself . The idea behind the paper is to discuss the facial recognition and features used to detect and alert drivers in real time if fatigue is detected . Face monitoring technique is used to identify state of the driver . This paper describes a novel method of facial representation and recognition based upon adaptive processing of tree structures . Instead of the conventional flat vector representation for a face , a neural network approach-based technique is proposed to transform the Localised Gabor Feature ( LGF ) vectors extracted from human facial components into Human Face Tree Structure ( HFTS ) to represent a human face . Please see reference therein for other existing works . For facial expression recognition in general , there is a trade - off between method simplicity and performance , i.e. , image - based vs. video - based methods . As videos are sequential signals , appearance - based methods including ours can not model the dynamics given by a temporal model or spatio - temporal models . document : DeXpression : Deep Convolutional Neural Network for Expression Recognition We propose a convolutional neural network ( CNN ) architecture for facial expression recognition . The proposed architecture is independent of any hand - crafted feature extraction and performs better than the earlier proposed convolutional neural network based approaches . We believe that second - order statistics such as covariance is better able to capture such distortions in regional facial features . In this work , we explore the benefits of using a manifold network structure for covariance pooling to improve facial expression recognition . In particular , we first employ such kind of manifold networks in conjunction with traditional convolutional networks for spatial pooling within individual image feature maps in an end - to - end deep learning manner . section : Conclusion In this work , we exploit the use of SPDNet on facial expression recognition problems . As shown above , SPDNet applied to covariance of convolutional features can classify facial expressions more efficiently . In contrast to basic - level recognition , fine - grained categorization aims to distinguish between different breeds or species or product models , and often requires distinctions that must be conditioned on the object pose for reliable identification . Facial recognition is the classic case of fine - grained recognition , and it is noteworthy that the best facial recognition methods jointly discover facial landmarks and extract features from those locations . Localizing the parts in an object is therefore central to establishing correspondence between object instances and discounting object pose variations and camera view position .
We propose an automatic 3D method for the determination of legal maturity from MR images based on the ossification of the radius bone . Age estimation is performed by a linear regression model of the epiphyseal gap volume over the known ground truth age of training data . Results are comparable with the established Greulich/Pyle ( GP ) and Tanner/Whitehouse ( TW ) methods , but do not involve harmful radiation .
Linear canonical correlation analysis ( CCA ) has been widely applied owing to low complexity , small and fixed amount of model parameters and good scalability . However , linear CCA based regression gets lower accuracy than its kernel version on the age estimation problem . The inexactness of metric distance information carried by age labels increases the complexity of using regression-based methods to estimate age .
Furthermore , a comparative study and critical analysis of the existing and recent algorithms is conducted . Hence the research gaps are identified for future work in age estimation problem .
Four different subsets of the MORPH-II database were selected for a wide range of purposes , including age estimate , gender and race classification , and facial recognition . Due to the variety of poses and lighting conditions present in the MORPH-II database , certain preprocessing techniques were used to standardize the images and make them more suitable for machine learning tasks .
However , they have their own characteristics in the algorithm by adopting diverse approaches to optimize the regression such as deep learning , Gaussian process , local binary features and so nonexperimental results show all of them can achieve improvements compare to state-of-art methods . With facial point detection , information of human face can be utilized for facial analysis applications , such as face recognition , face synthesis and age estimate .
This paper presents a method to estimate age from facial images . Firstly , we extract the facial features for age estimating automatically using AAM , then classify these facial features by different ages . In order to improve the precision of the classifying , we present a method which is Artificial Immune Recognition System based on Partial Hausdorff distance , describe the process of how to train the system and how to classify the facial features .
To show the effectiveness of proposed method , we first compare Relative Tree method with Relative Attribute on PubFig and OSR dataset . Then to verify the efficiency of Relative Forest algorithm , we conduct age estimation evaluation on FG-NET dataset . With much less training time compared to Relative Attribute and Relative Tree , proposed Relative Forest achieves state-of-the-art age estimation accuracy .
Age Estimation Components , i.e. the extraction and localization of facial features landmark points , which represent ageing pattern are very important steps in the age estimation process . A good age estimation technique will contain enough variation of landmark points and consider all important points to express the full complexity of the problem .
Different classification methods are proposed for automatic human age estimation in Malaysian population . This paper provide significant implications in the computation of fragmentary skeletal remains and forensic population samples for age estimation purpose .
Here , this problem is addressed by making the following contributions . First , in answer to one of the key problems of age estimation research — absence of data — a unique data set of face images , labelled for age and gender is offered , acquired by smart-phones and other mobile devices , and uploaded without manual filtering to online image repositories . The images in this collection are more challenging than those offered by other face-photo benchmarks .
Age estimation is an important method to solve the face recognition with age change , due to the feature extraction , in the process of age estimation study , PCA dimensional reduction method is usually used to reduce dimension with excessive dimension . PCA refers that transform the sample matrix into one-dimensional vector first , then the one-dimensional vectors form a matrix , solve the eigenvector .
In comparison with metric regression , which assumes that is a continuous random variable , ordinal regression regards as a finite sequence where the metric distance between ranks is not defined . Along with age estimation niu2016ordinal , popular applications for ordinal regression include predicting the progression of various diseases , such as Alzheimer ’s disease doyle2014predicting , Crohn ’s disease , artery disease , and kidney disease . Also , ordinal regression models are common choices for text message advertising and various recommender systems .
Our proposed method addresses both of these issues with a theoretical guarantee for classifier consistency as well as a reduction of the training complexity . subsection : CNN Architectures for Age Estimation Due to its broad utility in social networking , video surveillance , and biometric verification , age estimation from human faces is an area of active research .
It does this by using an R - CNN based approach and a modified AlexNet architecture which fuses intermediate convolutional layer outputs and adds separate fully - connected networks to predict each subtask . All - In - One Convolutional Neural Network for Face Analysis adds smile , age estimation and facial recognition to the former prediction tasks . We compare our results to all of these works .
Face hallucination refers to obtaining a clean face image from a degraded ones . The degraded face is assumed to be related to the clean face through the forward imaging model that account for blurring , sampling and noise .
Learning-based SR algorithms infer latent HR images by the guidance of coexisted priors from training samples . Various regularization methods have been successfully applied in face hallucination to ameliorate its ill-posed nature . But most of them only consider the local manifold geometry of a single patch which results in an unstable solution for SR reconstruction .
The utilization of this dependency relation together with component priors would regularize the solution more . In this work a fast synthesis based approach is proposed for the face hallucination problem by incorporating shape information , texture information and the statistical dependency of the image components simultaneously . Experiments show that more accurate reconstructions can be obtained with less computational effort .
In this paper , we propose a novel learning-based face hallucination framework built in DCT domain , which can recover the high-resolution face image from a single low-resolution one . Unlike most previous learning-based work , our approach addresses the face hallucination problem from a different angle . In details , the problem is formulated as inferring DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain .
In this paper , we propose a region-based eigentransformation method for human face hallucination . The algorithm first uses the snake method to locate the targeted regions with high-frequency features in an edge-smoothed face mask , and then employs the eigentransformation to reconstruct the corresponding high-resolution counterpart .
On the basis of this finding , we suggest a stepwise reconstruction scheme to minimize inconsistency risk in solution space . In particular , this scheme divides face hallucination process into multiple cascaded incremental training-synthesis steps , in which each individual step allows smaller magnifying factor as well as the corresponding intermediate resolution ( IR ) dictionary rather than merely LR and HR dictionary based learning . Moreover , in order to keep sparse representation ( SR ) sufficiently sparse while favoring its locality , we introduce a weighted ℓ1/ℓ2 mixed norms minimization SR method and formulate a unified framework together with stepwise scheme .
Additionally , they ignore the influence of spatial distances between the test image and training basis images on optimal reconstruction coefficients . Consequently , they can not offer a satisfactory performance in practical face hallucination applications . In this paper , we propose a weighted adaptive sparse regularization ( WASR ) method to promote accuracy , stability and robustness for face hallucination reconstruction , in which a distance-inducing weighted ℓq norm penalty is imposed on the solution . With the adjustment to shrinkage parameter q , the weighted ℓq penalty function enables elastic description ability in the sparse domain , leading to more conservative sparsity in an ascending order of q .
Therefore , it is difficult to generate facial attributes accurately . In this paper , we treat the sketch to face the problem as a face hallucination reconstruction problem . In order to solve this problem , we propose an image translation network by exploiting attributes with the generated adversarial network .
Subsequently , we formulate a Bayesian approach accounting for the statistical inference from sketches to their corresponding photos in terms of the learned tensor model . Comparative experiments are conducted to contrast the proposed method with state-of-the-art algorithms for facial sketch synthesis in a novel face hallucination scenario : sketch-based facial photo hallucination . The encouraging results obtained convincingly validate the effectiveness of our method .
For recognition purpose , we prefer to obtain near-frontal faces . To this end , we propose a nuclear norm regularized structural orthogonal Procrustes regression ( N2SOPR ) approach in this work to acquire pose-robust feature representations for face hallucination with pose . The orthogonal Procrustes regression is used to seek an appropriate transformation between two data matrixes .
In addition , in the residual-compensation process , a linear-mapping method is proposed to include both the inter- and intrainformation about manifolds of different resolutions . Compared with other state-of-the-art approaches , the proposed framework can achieve a comparable , or even better , performance in terms of global face reconstruction and the visual quality of face hallucination . Experiments on images with various parameter settings and blurring distortions show that the proposed approach is robust and has great potential for real-world applications .
[ reference ] . The external example - based methods can be formulated for generic image super - resolution , or can be designed to suit domain specific tasks , i.e. , face hallucination [ reference ] , [ reference ] , according to the training samples provided . The sparse - coding - based method [ reference ] , [ reference ] is one of the representative external example - based SR methods .
Furthermore , a denoising network is presented to serve for perceptual regularization to suppress the artifacts in transfer result , while an attribute ratio regularization is introduced to constrain the size of attribute relevant region . Our DIAT can provide a unified solution for several representative facial attribute transfer tasks , e . g . , expression transfer , accessory removal , age progression and gender transfer , and can be extended for other face enhancement tasks such as face hallucination . The experimental results validate the effectiveness of the proposed method .
Here we use the face hallucination as an example . Face hallucination is undoubtedly a global transfer task , and thus we remove the mask network as well as the attribute ratio regularization , making . Moreover , the input image in face hallucination is of low resolution ( LR ) while the output image is of high resolution ( HR ) .
For example , it is able to predict the gender . Recently , the gender prediction based on handwriting becomes an interesting research . Even in 2013 , an competition for prediction gender from handwriting has been held by Kaggle .
The proposed method predicts race and gender from a single eye image with an accuracy of 86 % and 90 % , respectively . In addition , the following analysis are conducted : ( a ) the role of different parts of the ocular region on attribute prediction ; ( b ) the influence of gender on race prediction , and vice-versa ; ( c ) the impact of eye color on gender and race prediction ; ( d ) the impact of image blur on gender and race prediction ; ( e ) the generalizability of the method across different datasets , i.e. , cross-dataset performance ; and ( f ) the consistency of prediction performance across the left and right eyes .
The model applied by using big data technology and achieved 85.6 % accuracy in terms of user gender prediction and 65.5 % of user age prediction . The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain .
We then show that this knowledge can be used in the analysis of first-person communication ; knowledge of distinguishing attributes allows us to both classify users and to bootstrap new training examples . Our novel approach enables substantial improvements on the widelystudied task of user gender prediction , obtaining a 20 % relative error reduction over the current state-of-the-art .
The CLIN 29 shared task is concerned with binary gender prediction within and across different genres in Dutch . Our proposed approaches to this problem are a simple model , which uses character ngrams , and a more complex model which consists of two systems in a co-training setup .
The first one ( VGG16/10 ) considers gender and age as two related characteristics and the final neurons have been conceived to hold these aspects together at the same time . The second one ( VGG16/8 + 1 ) in the prediction layer has got a neuron for the prediction of gender and another eight for the prediction of age ( according to the Adience benchmark ) . Such networks have been conceived to provide simultaneously information on both the gender and age of the person identified in the image , without the need to build two dedicated networks .
Apart from human recognition , gait has lately become a promising biometric feature also useful for prediction of gender . One of the most popular methods to represent gait is the well-known Gait Energy Image ( GEI ) , which conducts to a high-dimensional Euclidean space where many features are irrelevant .
The task of predicting demographics of social media users , bloggers and authors of other types of online texts is crucial for marketing , security , etc . However , most of the papers in authorship profiling deal with author gender prediction . In addition , most of the studies are performed in English-language corpora and very little work in the area in the Russian language .
This paper introduces an empirical investigation which directly addresses and explores gender prediction capacity from digitised handwriting data from several different perspectives - such as feature type ( static/dynamic ) and content ( fixed/variable ) types - in order to provide extensive experimental evidence and analysis to guide the development of a better understanding of the opportunities for and practical consequences of gender prediction from digital handwriting data .
Term popularity and discriminability is important for on-device gender prediction . With the proliferation of mobile devices and the growing necessity for gender information in personalized intelligent systems , gender prediction of mobile users has become an important research issue . Text data in mobile devices are known to have high discriminative power for gender , but transmitting those data to the outside of a device has a security risk and raises a privacy concern of users .
The text data consists of articles that the users have read that are representative of the minority class . Our goal is to improve the predictive power of supervised learning algorithms for the gender prediction problem , using articles consumed by the user from a large health-based website as our data source . Our study shows that by creating synthetic user profiles for the minority class with SeqGANs and passing in the resampled training data to an XGBoost classifier , we achieve a gain of 2 % in AUROC , as well as a 3 % gain in both F1-Score and AUPR for gender prediction when compared to SMOTE .
Age distribution learning has made other notable progress in age estimation ; here , the researchers defined a new loss function to penalize the difference between estimated age distributions and the ground truth age labels . Recent research has also shown that training a multi - task CNN for various face analysis tasks , including face detection , gender prediction , age estimation , etc . , can improve the overall performance across different tasks compared to a single - task CNN ranjan2017all by sharing lower - layer parameters . In chen2016cascaded , a cascaded convolutional neural network was designed to classify face images into age groups followed by regression modules for more accurate age estimation .
Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose , speech , facial expression , behaviour and actions which are used in conveying information about the intentions and emotions of a human being . In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis ( PCA ) is used for dimensionality reduction .
In this paper , we propose a null space discriminant locality preserving projections ( NDLPP ) method for facial feature extraction and recognition . Based on locality preserving projections ( LPP ) and discriminant locality preserving projections ( DLPP ) methods , NDLPP comes into the characteristics of DLPP that encodes both the geometrical and discriminant structure of the data manifold , and addresses the small sample size problem by solving an eigenvalue problem in null space .
Facial recognition and verification is a widely used biometric technology in security system . Unfortunately , face biometrics is vulnerable to spoofing attacks using photographs or videos .
Facial landmark localization is important to many facial recognition and analysis tasks , such as face attributes analysis , head pose estimation , 3D face modeling , and facial expression analysis . In this paper , we propose a new approach to localizing landmarks in facial image by deep convolutional neural network ( DCNN ) .
Facial landmark localization plays a critical role in facial recognition and analysis . In this chapter , we first propose a novel cascaded backbone-branches fully convolutional neural network ( BB-FCN ) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings .
Distributed Simulation of Modular Neural Networks.- Dynamical Behavior of an Electronic Neuron of Commutation.- Training Neural Networks Using Non-standard Norms -Knowledge Representation and Reasoning.- Searching for a Solution to Program Verification = Equation Solving in CCS.- Knowledge Representation Using High-Level Non-monotonic Reasoning.- Experiments on Information Retrieval Using Case-Based Reasoning.- A Probabilistic Exemplar-Based Model for Case-Based Reasoning.- Intensification and Diversification Strategies with Tabu Search : One-Machine Problem with Weighted Tardiness Objective.- A Methodology to Parallel the Temperature Cycle in Simulated Annealing.- Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using the Threshold Accepting Algorithm.- A Practical Approach for Logic Program Analysis and Transformation.- Experiments in Answer Sets Planning.- Robotics and Computer Vision.- Competitive Learning Methods for Efficient Vector Quantizations in a Speech Recognition Environment.- Facial Expression Recognition and Modeling for Virtual Intelligent Tutoring Systems.- Detection and Tracking of Facial Features in Video Sequences.- An Application of Behavior-Based Architecture for Mobile Robots Design.- Cooperative Simulated Annealing for Path Planning in Multi-robot Systems.- Learning Probabilistic Grid-Based Maps for Indoor Mobile Robots Using Ultrasonic and Laser Range Sensors.- Evolving Insect Locomotion Using Cooperative Genetic Programming.- On the Minimal Cooperative Attitude in Multi-robotics Systems.- Eigenfaces Versus Eigeneyes :
Facial analysis and recognition is one of the most popular information processing tasks . Face recognition requires some form of dimensionality reduction in the face dataset .
To overcome these limitations and take full advantage of the spatio-temporal information , we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events . We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition . Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles , as well as its advantage over the existing methods .
This paper presents a method for facial feature representation and recognition from the Covariance Matrices of the Gabor-filtered images . Gabor filters are a very powerful tool for processing images that respond to different local orientations and wave numbers around points of interest , especially on the local features on the face .
Driver fatigue can also be categorized under driver behavioral as it comes from the driver itself . The idea behind the paper is to discuss the facial recognition and features used to detect and alert drivers in real time if fatigue is detected . Face monitoring technique is used to identify state of the driver .
This paper describes a novel method of facial representation and recognition based upon adaptive processing of tree structures . Instead of the conventional flat vector representation for a face , a neural network approach-based technique is proposed to transform the Localised Gabor Feature ( LGF ) vectors extracted from human facial components into Human Face Tree Structure ( HFTS ) to represent a human face .
Please see reference therein for other existing works . For facial expression recognition in general , there is a trade - off between method simplicity and performance , i.e. , image - based vs. video - based methods . As videos are sequential signals , appearance - based methods including ours can not model the dynamics given by a temporal model or spatio - temporal models .
document : DeXpression : Deep Convolutional Neural Network for Expression Recognition We propose a convolutional neural network ( CNN ) architecture for facial expression recognition . The proposed architecture is independent of any hand - crafted feature extraction and performs better than the earlier proposed convolutional neural network based approaches .
We believe that second - order statistics such as covariance is better able to capture such distortions in regional facial features . In this work , we explore the benefits of using a manifold network structure for covariance pooling to improve facial expression recognition . In particular , we first employ such kind of manifold networks in conjunction with traditional convolutional networks for spatial pooling within individual image feature maps in an end - to - end deep learning manner .
section : Conclusion In this work , we exploit the use of SPDNet on facial expression recognition problems . As shown above , SPDNet applied to covariance of convolutional features can classify facial expressions more efficiently .
In contrast to basic - level recognition , fine - grained categorization aims to distinguish between different breeds or species or product models , and often requires distinctions that must be conditioned on the object pose for reliable identification . Facial recognition is the classic case of fine - grained recognition , and it is noteworthy that the best facial recognition methods jointly discover facial landmarks and extract features from those locations . Localizing the parts in an object is therefore central to establishing correspondence between object instances and discounting object pose variations and camera view position .
Determination of legal majority age from 3D magnetic resonance images of the radius bone Linear canonical correlation analysis based ranking approach for facial age estimation Age estimation using facial images: A survey of the state-of-the-art MORPH-II: Feature vector documentation Analysis of Methods for Facial Landmark Detection Age Estimation from Facial Images Based on Artificial Immune Recognition System Relative Forest for Attribute Prediction Automated Facial Features Points Localization for Age Estimation Based on Ideal Frontal Symmetry and Proportion of the Face Automated human age estimation at death via bone microstructures Texture-based Estimation of Age and Gender from Wild Conditions Research on Age Estimation by Means of 2D-PCA Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks Fine-Grained Head Pose Estimation Without Keypoints Adaptive Aggregation Network for Face Hallucination Group Embedding for Face Hallucination Efficient face hallucination by using shape and texture dependency Learning-based face hallucination in DCT domain Region-based eigentransformation for face image hallucination Face hallucination based on stepwise sparse reconstruction Face Hallucination Via Weighted Adaptive Sparse Regularization Generating Photographic Faces From the Sketch Guided by Attribute Using GAN Bayesian Tensor Inference for Sketch-Based Facial Photo Hallucination Face image super-resolution with pose via nuclear norm regularized structural orthogonal Procrustes regression Face hallucination using orthogonal canonical correlation analysis Image Super-Resolution Using Deep Convolutional Networks Deep Identity-aware Transfer of Facial Attributes Fuzzy Rule-based Classification Systems for the Gender Prediction from Handwriting Predicting Gender and Race from Near Infrared Iris and Periocular Images Predicting customer’s gender and age depending on mobile phone data Using Conceptual Class Attributes to Characterize Social Media Users Co-Training vs. Simple SVM Comparing Two Approaches for Cross-Genre Gender Prediction Age & Gender Classifier for Edge Computing Efficient Gait-based Gender Classification through Feature Selection Profiling the Age of Russian Bloggers Exploring gender prediction from digital handwriting An on-device gender prediction method for mobile users using representative wordsets Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis Null space discriminant locality preserving projections for face recognition Anisotropic Diffusion-based Kernel Matrix Model for Face Liveness Detection Facial landmark localization by enhanced convolutional neural network Face Localization and Enhancement MICAI 2000 : advances in artificial intelligence : Mexican International Conference on Artificial Intelligence, Acapulco, Mexico, April 11-14, 2000 : proceedings A Comprehensive Comparative PerformanceAnalysis of Eigenfaces, Laplacianfaces andOrthogonal Laplacianfaces for Face Recognition Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition Global Feature Extraction and Recognition from Matrices of Gabor Feature Faces Fatigue Alert System Human face recognition by adaptive processing of tree structures representation Transferring Face Verification Nets To Pain and Expression Regression DeXpression: Deep Convolutional Neural Network for Expression Recognition Covariance Pooling for Facial Expression Recognition Part-Based R-CNNs for Fine-Grained Category Detection
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