Recently a tree structure model for multi-view face detection was proposed . This method is primarily designed for facial landmark detection and consequently a face detection is provided . However , the effort to model inner facial structures by using a detailed facial landmark labelling resulted on a complex and suboptimal system for face detection . Facial landmark detection , as a typical and crucial task in computer vision , is widely used in face recognition , face animation , facial expression analysis , etc . In the past decades , many efforts are devoted to designing robust facial landmark detection algorithms . However , such techniques have shown low accuracy , especially in the exceptional conditions such as low luminance or overlapped face . To overcome this problem , we propose a new facial landmark extraction scheme using deep learning and semantic segmentation and demonstrate that with even small dataset , our scheme can achieve excellent facial landmark extraction performance . Hence , the quantification of the error associated to the location of facial landmarks seems to be necessary when photographs become a key element of the forensic procedure . In this work , we statistically evaluate the inter- and intra-observer dispersions related to the facial landmark identification on photographs . In the inter-observer experiment , a set of 18 facial landmarks was provided to 39 operators . Our scheme aims at incorporating the learning of the output representation y in the training process in an unsupervised fashion while learning the supervised mapping function x → y. We evaluate our framework on a facial landmark detection problem which is a typical structured output task . We show over two public challenging datasets ( LFPW and HELEN ) that our regularization scheme improves the generalization of deep neural networks and accelerates their training . The main contribution of this work is the compression of heavy baseline model to a lightweight model . Moreover , minimal network structure is designed based on facial landmark key point detection to recognize whether the driver is drowsy . The proposed model is able to achieve an accuracy of more than 80 % . Handling pose is a classical problem in facial analysis . Recently , parts-based models have shown promising performance for facial landmark points detection ` in the wild ' . Leveraging on the success of these models , the proposed data-driven regression framework computes a constrained normalised virtual frontal head pose . In this paper we present a framework for dynamic facial landmarking selection based on facial expression analysis using Gaussian Processes . We perform facial features tracking , based on Active Appearance Models for facial landmarking detection , and then use Gaussian process ranking over the dynamic emotional sequences with the aim to establish which landmarks are more relevant for emotional multivariate time-series recognition . The experimental results show that Gaussian Processes can effectively fit to an emotional time-series and the ranking process with log-likelihoods finds the best landmarks ( mouth and eyebrows regions ) that represent a given facial expression sequence . They use the coarse pose supervision from the AFLW dataset in order to improve landmark detection . Two works dwell on building one network to fulfill various prediction tasks regarding facial analysis . Hyperface is a CNN that sets out to detect faces , determine gender , find landmarks and estimate head pose at once . Chang et al . also argue for landmark - free head pose estimation . They regress 3D head pose using a simple CNN and focus on facial alignment using the predicted head pose . They demonstrate the success of their approach by improving facial recognition accuracy using their facial alignment pipeline . The same testing protocol is adopted for all compared methods and numbers are reported directly from the associated papers . Hyperface and All - In - One both use a single network for numerous facial analysis tasks . Hyperface uses an AlexNet pre - trained on ImageNet as a backbone and document : Nonlinear 3D Face Morphable Model As a classic statistical model of 3D facial shape and texture , 3D Morphable Model ( 3DMM ) is widely used in facial analysis , e.g. , model fitting , image synthesis . Conventional 3DMM is learned from a set of well - controlled 2D face images with associated 3D face scans , and represented by two sets of PCA basis functions . section : Related Work In this section , we review the recent developments on designing convolutional network architectures , quantizing the neural networks , human pose estimation and facial landmark localization . Network Architecture . In contrast , we design a simple yet very effective connectivity pattern for stacked U - Nets . Facial Landmark Localization . Similarly , CNNs have largely reshaped the field of facial landmark localization . We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image . We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions . Code and models will be made available at http: // aaronsplace.co.uk [ enumerate ] leftmargin=* , itemsep= - 1 mm [ itemize ] leftmargin=* , itemsep= - 1 mm RAR augments training images with occlusions incurred by outside natural objects like sunglasses , phones , and hands . FLD + PDE performs facial landmark detection , pose and deformation estimation simultaneously , in which the training data of pose and deformation estimation are used . Other methods including our MCL only utilize given training images from the benchmarks . section : Introduction Facial landmark detection is rarely , if ever , an application in its own right . Instead , it is typically a means to an end : It is one component out of many in pipelines designed for other face understanding and processing tasks , often providing effective means for aligning face photos and making them easier to process . Applications of facial landmark detectors . Facial landmark detection is big business , as reflected by the numerous citation to relevant papers , the many facial landmark detection benchmarks , and popular international events dedicated to this problem . With all this effort , a rigorous survey of the many applications of facial landmarks is outside the scope of this paper . In lieu of such a survey , and to get some idea of why this problem attracts so much attention , we offer the following cursory study . We propose a far simpler alternative and show it to result in robust and accurate face alignment . section : A critique of facial landmark detection Before using an existing state - of - the - art facial landmark detector in a face processing system , the following points should be considered . This difference is due to the fact that StarGAN - JNT learns to translate CelebA images during training but not StarGAN - SNG . In other words , StarGAN - JNT can leverage both datasets to improve shared low - level tasks such facial keypoint detection and segmentation . By utilizing both CelebA and RaFD , Furthermore , this study shows that 2D alignment does not provide much benefit on IJB - A , in contrast with reported performance on near frontal datasets . One hypothesis is that this is due to the fact that this dataset has many profile faces for which facial landmark alignment is inaccurate or fails altogether . subsection : Negative Set Study This chapter provides a summary of local operators recently proposed for heterogeneous face recognition . It also analyzes performance of each individual operator and demonstrates performance of composite operators . The task of Heterogeneous Face Recognition consists in matching face images that are sensed in different domains , such as sketches to photographs ( visual spectra images ) , and thermal images to photographs or near-infrared images to photographs . In this paper , we suggest that the high-level features of Deep Convolutional Neural Networks trained in visual spectra images are potentially domain independent and can be used to encode faces sensed in different image domains . Therefore , the LBP features of filtered face images are more discriminative for representation based classifiers . Furthermore , we also extend our algorithm for heterogeneous face recognition problem . Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm . This paper considers a heterogeneous face recognition problem , i.e. , matching near-infrared ( NIR ) to visible ( VIS ) face images . The significant domain gap between the NIR and VIS modalities poses great challenges to accurate face recognition . Thus , it is important to design algorithms that are able to recognize objects from distinct views . To the end , a large number of approaches have been proposed to achieve the heterogeneous recognition tasks through the use of local features . However , most of them only focus on binary views and thus can not be applied to multi-view analysis . Finally , Voting ELM is utilized as the classifier to improve the recognition accuracy and to speed up the feature learning process . Experiments conducted on two different heterogeneous face recognition scenarios demonstrate the effectiveness of our proposed approach . Based on the encoded face images , we further develop a discriminant matching method to infer the hidden identity information of the cross-modality face images for enhanced recognition performance . The effectiveness of the proposed approach is demonstrated ( on several public-domain face datasets ) in two typical heterogeneous face recognition scenarios : matching NIR faces to VIS faces and matching sketches to photographs . This paper reports a novel approach for heterogeneous face identification which makes use of fusion of MB-LBP ( Multi-Block Local Binary Pattern ) and MLBP ( Modified Local Binary Pattern ) descriptors at feature extraction level . Face images obtained from different sources such as visible , sketch ( viewed and forensics ) and NIR ( Near Infrared ) face images are used for the experiment . With the help of the learned filters , CDFL can maximize the interclass variations and minimize the intraclass variations of the learned feature vectors , and meanwhile maximize the correlation of face images of the same person from different modalities by solving a generalized eigenvalue problem . Experimental results on three different heterogeneous face recognition applications show the effectiveness of our proposed approach . Matching near-infrared to visible images is one of the heterogeneous face recognition challenges in which spectral variations cause changes in the appearance of face images . In this paper , we propose to utilize a keypoint selection approach in the recognition pipeline . Composite sketch recognition belongs to heterogeneous face recognition research , which is of great important in the field of criminal investigation . Because composite face sketch and photo belong to different modalities , robust representation of face feature cross different modalities is the key to recognition . The method comprises the steps of converting a near-infrared human face image or a visible light human face photo into an initial visible light human face photo or an initial near-infrared human face image by use of a method based on sparse learning , and converting the initial visible light human face photo or the initial near-infrared human face image into a high-definition detail photo of the visible light human face photo or the near-infrared human face image by use of a method based on multi-characteristic selection . According to the mutual conversion method , heterogeneous human face images are fitted in a stratified manner by use of a method based on sparse regularization and the visible light human face photo is generated from the near-infrared human face image , and therefore , the detail information of the synthetic photo is increased and the problem of heterogeneous human face recognition is solved . Suppose we have a cross - view training set of classes , where contains samples in a - dimensional space from one view , contains samples in the same - dimensional space but from the other view . The cross - view matching problem arises from many applications , like heterogeneous face recognition and viewpoint invariant person re - identification . Note that is the same with in the single - view matching scenario . We show that by introducing multiple fake class and oversampling , the model can generate the required minority samples . We evaluate our model on face generation task from attributes using a reduced number of samples in the minority class . Results obtained showed that MFC-GAN produces plausible minority samples that improve the classification performance compared with state-of-the-art AC-GAN generated samples . Face Reconstruction is an important research area in field of Computer Vision and Artificial Intelligence , and has a wide range of related applications in real life . Previous work primarily focuses on face feature extraction through images of real faces , in contrast , our work reconsiders generating real faces with discernible features based on sketch and outlines , in order to be used for face super-resolution reconstruction and face generation , and ultimately for feature face generation using CAN , such as generating real faces according to the descriptions of suspect 's facial features by the police . The research content in this paper includes an algorithm for Asian people 's face generation without gender bias , a method for generating feature face data sample through digital image processing , and an innovative CAN architecture . This paper also uses a hyper-parameter to measure the diversity and quality of the generated image . The experimental results of the model on the CelebA dataset show that the model has excellent performance in face image generation . Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech . This is a challenging task because face appearance variation and semantics of speech are coupled together in the subtle movements of the talking face regions . To this end , there has been some success in speech-driven lip motion generation or talking face generation . Among them , talking face generation aims to generate realistic talking heads synchronized with the audio or text input . This task requires mining the relationship between audio signal/text and lip-sync video frames and ensures the temporal continuity between frames . Secondly , a combined solution is proposed to integrate local and global sparsity methods . Thirdly we customize proposed solution to fit in two deep learning networks : deep belief network ( DBN ) and generative adversarial network ( GAN ) , and then test on benchmark datasets MNIST and CelebA. Experimental results show that our method outperforms existing sparsity algorithm on digits recognition , and achieves a better performance on human face generation . Additionally , proposed method could also stabilize GAN loss changes and eliminate noises . Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images , hardly distinguishable from real ones . In particular , human face generation achieved a stunning level of realism , opening new opportunities for the creative industry but , at the same time , new scary scenarios where such content can be maliciously misused . Therefore , it is essential to develop innovative methodologies to automatically tell apart real from computer generated multimedia , possibly able to follow the evolution and continuous improvement of data in terms of quality and realism . Secondly , a realistic 3D face will be created interactively based on the participation of the user . The users participate in the model adjustment and texture specified in the process of face generate . Finally , experiment results show that a realistic 3D face model could be generated by this approach . This paper provides a comprehensive overview of recent developments of 3D face modeling and keeps up with the latest research mainly from 1990 to now . Different from previous reviews , this paper puts emphasis on four major issues involved in a general frame work of 3D face modeling , i.e. 3D capture , generic 3D face modeling , individual face model generation and its application . This paper focuses more on overall methods and general characteristics involved in above four issues . In this paper , we propose a face set model generation method from 3D point clouds obtained from 3D camera for high-speed and light-weight storing and showing environment information in tele-operation task for robots . In the proposed method , following procedures run in parallel ; 3 dominant orthogonal axis estimation and point cloud grouping by normal vectors based on the Manhattan - world assumption , fast registration using dominant axis grouped point cloud , plane position estimation for each dominant axis group , and face set generation by shape estimation for each plane . Experimental results shows that accuracy of plane position estimation is equivalent to measurement accuracy , registration takes about 0.1[s ] for each frame , and storage size is reduced to about 10 - 20 % from original 3D point cloud size . RegCGAN is based on the conditional GAN , and we introduce two regularizers to guide the model to learn the corresponding semantics of different domains . We evaluate the proposed model on several tasks for which paired training data is not given , including the generation of edges and photos , the generation of faces with different attributes , etc . The experimental results show that our model can successfully generate corresponding images for all these tasks , while outperforms the baseline methods . In this paper , we propose a novel multi-component deep convolutional neural network ( CNN ) based architecture which performs the complex task of makeup removal from a disguised face , to reveal the original mugshot image of the impostor ( i.e. without makeup ) . The proposed network also performs the hard tasks of FR on a disguised face in addition to recognition of identity and generation of the face of the spoofed target , by minimizing a novel multi-component objective function . Comparison of performance with a few recent state-of-the-art methods of FR over three benchmark datasets reveals the superiority of our proposed method for both synthesis as well as recognition ( FR ) tasks . In this paper , we propose to incorporate the face-based age recognition technology to ticket inspection to solve this kind of problem , which could also greatly improve the efficiency of ticket checking . In this paper , we try to fine tuning the Inception ResnetV2 model , by replacing the original simple convolutional neural network for the facial feature extraction and recognition . The experiment results suggest that the accuracy of age recognition has been significantly improved .
Recently a tree structure model for multi-view face detection was proposed . This method is primarily designed for facial landmark detection and consequently a face detection is provided . However , the effort to model inner facial structures by using a detailed facial landmark labelling resulted on a complex and suboptimal system for face detection .
Facial landmark detection , as a typical and crucial task in computer vision , is widely used in face recognition , face animation , facial expression analysis , etc . In the past decades , many efforts are devoted to designing robust facial landmark detection algorithms .
However , such techniques have shown low accuracy , especially in the exceptional conditions such as low luminance or overlapped face . To overcome this problem , we propose a new facial landmark extraction scheme using deep learning and semantic segmentation and demonstrate that with even small dataset , our scheme can achieve excellent facial landmark extraction performance .
Hence , the quantification of the error associated to the location of facial landmarks seems to be necessary when photographs become a key element of the forensic procedure . In this work , we statistically evaluate the inter- and intra-observer dispersions related to the facial landmark identification on photographs . In the inter-observer experiment , a set of 18 facial landmarks was provided to 39 operators .
Our scheme aims at incorporating the learning of the output representation y in the training process in an unsupervised fashion while learning the supervised mapping function x → y. We evaluate our framework on a facial landmark detection problem which is a typical structured output task . We show over two public challenging datasets ( LFPW and HELEN ) that our regularization scheme improves the generalization of deep neural networks and accelerates their training .
The main contribution of this work is the compression of heavy baseline model to a lightweight model . Moreover , minimal network structure is designed based on facial landmark key point detection to recognize whether the driver is drowsy . The proposed model is able to achieve an accuracy of more than 80 % .
Handling pose is a classical problem in facial analysis . Recently , parts-based models have shown promising performance for facial landmark points detection ` in the wild ' . Leveraging on the success of these models , the proposed data-driven regression framework computes a constrained normalised virtual frontal head pose .
In this paper we present a framework for dynamic facial landmarking selection based on facial expression analysis using Gaussian Processes . We perform facial features tracking , based on Active Appearance Models for facial landmarking detection , and then use Gaussian process ranking over the dynamic emotional sequences with the aim to establish which landmarks are more relevant for emotional multivariate time-series recognition . The experimental results show that Gaussian Processes can effectively fit to an emotional time-series and the ranking process with log-likelihoods finds the best landmarks ( mouth and eyebrows regions ) that represent a given facial expression sequence .
They use the coarse pose supervision from the AFLW dataset in order to improve landmark detection . Two works dwell on building one network to fulfill various prediction tasks regarding facial analysis . Hyperface is a CNN that sets out to detect faces , determine gender , find landmarks and estimate head pose at once .
Chang et al . also argue for landmark - free head pose estimation . They regress 3D head pose using a simple CNN and focus on facial alignment using the predicted head pose . They demonstrate the success of their approach by improving facial recognition accuracy using their facial alignment pipeline .
The same testing protocol is adopted for all compared methods and numbers are reported directly from the associated papers . Hyperface and All - In - One both use a single network for numerous facial analysis tasks . Hyperface uses an AlexNet pre - trained on ImageNet as a backbone and
document : Nonlinear 3D Face Morphable Model As a classic statistical model of 3D facial shape and texture , 3D Morphable Model ( 3DMM ) is widely used in facial analysis , e.g. , model fitting , image synthesis . Conventional 3DMM is learned from a set of well - controlled 2D face images with associated 3D face scans , and represented by two sets of PCA basis functions .
section : Related Work In this section , we review the recent developments on designing convolutional network architectures , quantizing the neural networks , human pose estimation and facial landmark localization . Network Architecture .
In contrast , we design a simple yet very effective connectivity pattern for stacked U - Nets . Facial Landmark Localization . Similarly , CNNs have largely reshaped the field of facial landmark localization .
We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image . We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality , especially for the cases of large poses and facial expressions . Code and models will be made available at http: // aaronsplace.co.uk [ enumerate ] leftmargin=* , itemsep= - 1 mm [ itemize ] leftmargin=* , itemsep= - 1 mm
RAR augments training images with occlusions incurred by outside natural objects like sunglasses , phones , and hands . FLD + PDE performs facial landmark detection , pose and deformation estimation simultaneously , in which the training data of pose and deformation estimation are used . Other methods including our MCL only utilize given training images from the benchmarks .
section : Introduction Facial landmark detection is rarely , if ever , an application in its own right . Instead , it is typically a means to an end : It is one component out of many in pipelines designed for other face understanding and processing tasks , often providing effective means for aligning face photos and making them easier to process .
Applications of facial landmark detectors . Facial landmark detection is big business , as reflected by the numerous citation to relevant papers , the many facial landmark detection benchmarks , and popular international events dedicated to this problem . With all this effort , a rigorous survey of the many applications of facial landmarks is outside the scope of this paper . In lieu of such a survey , and to get some idea of why this problem attracts so much attention , we offer the following cursory study .
We propose a far simpler alternative and show it to result in robust and accurate face alignment . section : A critique of facial landmark detection Before using an existing state - of - the - art facial landmark detector in a face processing system , the following points should be considered .
This difference is due to the fact that StarGAN - JNT learns to translate CelebA images during training but not StarGAN - SNG . In other words , StarGAN - JNT can leverage both datasets to improve shared low - level tasks such facial keypoint detection and segmentation . By utilizing both CelebA and RaFD ,
Furthermore , this study shows that 2D alignment does not provide much benefit on IJB - A , in contrast with reported performance on near frontal datasets . One hypothesis is that this is due to the fact that this dataset has many profile faces for which facial landmark alignment is inaccurate or fails altogether . subsection : Negative Set Study
This chapter provides a summary of local operators recently proposed for heterogeneous face recognition . It also analyzes performance of each individual operator and demonstrates performance of composite operators .
The task of Heterogeneous Face Recognition consists in matching face images that are sensed in different domains , such as sketches to photographs ( visual spectra images ) , and thermal images to photographs or near-infrared images to photographs . In this paper , we suggest that the high-level features of Deep Convolutional Neural Networks trained in visual spectra images are potentially domain independent and can be used to encode faces sensed in different image domains .
Therefore , the LBP features of filtered face images are more discriminative for representation based classifiers . Furthermore , we also extend our algorithm for heterogeneous face recognition problem . Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm .
This paper considers a heterogeneous face recognition problem , i.e. , matching near-infrared ( NIR ) to visible ( VIS ) face images . The significant domain gap between the NIR and VIS modalities poses great challenges to accurate face recognition .
Thus , it is important to design algorithms that are able to recognize objects from distinct views . To the end , a large number of approaches have been proposed to achieve the heterogeneous recognition tasks through the use of local features . However , most of them only focus on binary views and thus can not be applied to multi-view analysis .
Finally , Voting ELM is utilized as the classifier to improve the recognition accuracy and to speed up the feature learning process . Experiments conducted on two different heterogeneous face recognition scenarios demonstrate the effectiveness of our proposed approach .
Based on the encoded face images , we further develop a discriminant matching method to infer the hidden identity information of the cross-modality face images for enhanced recognition performance . The effectiveness of the proposed approach is demonstrated ( on several public-domain face datasets ) in two typical heterogeneous face recognition scenarios : matching NIR faces to VIS faces and matching sketches to photographs .
This paper reports a novel approach for heterogeneous face identification which makes use of fusion of MB-LBP ( Multi-Block Local Binary Pattern ) and MLBP ( Modified Local Binary Pattern ) descriptors at feature extraction level . Face images obtained from different sources such as visible , sketch ( viewed and forensics ) and NIR ( Near Infrared ) face images are used for the experiment .
With the help of the learned filters , CDFL can maximize the interclass variations and minimize the intraclass variations of the learned feature vectors , and meanwhile maximize the correlation of face images of the same person from different modalities by solving a generalized eigenvalue problem . Experimental results on three different heterogeneous face recognition applications show the effectiveness of our proposed approach .
Matching near-infrared to visible images is one of the heterogeneous face recognition challenges in which spectral variations cause changes in the appearance of face images . In this paper , we propose to utilize a keypoint selection approach in the recognition pipeline .
Composite sketch recognition belongs to heterogeneous face recognition research , which is of great important in the field of criminal investigation . Because composite face sketch and photo belong to different modalities , robust representation of face feature cross different modalities is the key to recognition .
The method comprises the steps of converting a near-infrared human face image or a visible light human face photo into an initial visible light human face photo or an initial near-infrared human face image by use of a method based on sparse learning , and converting the initial visible light human face photo or the initial near-infrared human face image into a high-definition detail photo of the visible light human face photo or the near-infrared human face image by use of a method based on multi-characteristic selection . According to the mutual conversion method , heterogeneous human face images are fitted in a stratified manner by use of a method based on sparse regularization and the visible light human face photo is generated from the near-infrared human face image , and therefore , the detail information of the synthetic photo is increased and the problem of heterogeneous human face recognition is solved .
Suppose we have a cross - view training set of classes , where contains samples in a - dimensional space from one view , contains samples in the same - dimensional space but from the other view . The cross - view matching problem arises from many applications , like heterogeneous face recognition and viewpoint invariant person re - identification . Note that is the same with in the single - view matching scenario .
We show that by introducing multiple fake class and oversampling , the model can generate the required minority samples . We evaluate our model on face generation task from attributes using a reduced number of samples in the minority class . Results obtained showed that MFC-GAN produces plausible minority samples that improve the classification performance compared with state-of-the-art AC-GAN generated samples .
Face Reconstruction is an important research area in field of Computer Vision and Artificial Intelligence , and has a wide range of related applications in real life . Previous work primarily focuses on face feature extraction through images of real faces , in contrast , our work reconsiders generating real faces with discernible features based on sketch and outlines , in order to be used for face super-resolution reconstruction and face generation , and ultimately for feature face generation using CAN , such as generating real faces according to the descriptions of suspect 's facial features by the police . The research content in this paper includes an algorithm for Asian people 's face generation without gender bias , a method for generating feature face data sample through digital image processing , and an innovative CAN architecture .
This paper also uses a hyper-parameter to measure the diversity and quality of the generated image . The experimental results of the model on the CelebA dataset show that the model has excellent performance in face image generation .
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech . This is a challenging task because face appearance variation and semantics of speech are coupled together in the subtle movements of the talking face regions .
To this end , there has been some success in speech-driven lip motion generation or talking face generation . Among them , talking face generation aims to generate realistic talking heads synchronized with the audio or text input . This task requires mining the relationship between audio signal/text and lip-sync video frames and ensures the temporal continuity between frames .
Secondly , a combined solution is proposed to integrate local and global sparsity methods . Thirdly we customize proposed solution to fit in two deep learning networks : deep belief network ( DBN ) and generative adversarial network ( GAN ) , and then test on benchmark datasets MNIST and CelebA. Experimental results show that our method outperforms existing sparsity algorithm on digits recognition , and achieves a better performance on human face generation . Additionally , proposed method could also stabilize GAN loss changes and eliminate noises .
Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images , hardly distinguishable from real ones . In particular , human face generation achieved a stunning level of realism , opening new opportunities for the creative industry but , at the same time , new scary scenarios where such content can be maliciously misused . Therefore , it is essential to develop innovative methodologies to automatically tell apart real from computer generated multimedia , possibly able to follow the evolution and continuous improvement of data in terms of quality and realism .
Secondly , a realistic 3D face will be created interactively based on the participation of the user . The users participate in the model adjustment and texture specified in the process of face generate . Finally , experiment results show that a realistic 3D face model could be generated by this approach .
This paper provides a comprehensive overview of recent developments of 3D face modeling and keeps up with the latest research mainly from 1990 to now . Different from previous reviews , this paper puts emphasis on four major issues involved in a general frame work of 3D face modeling , i.e. 3D capture , generic 3D face modeling , individual face model generation and its application . This paper focuses more on overall methods and general characteristics involved in above four issues .
In this paper , we propose a face set model generation method from 3D point clouds obtained from 3D camera for high-speed and light-weight storing and showing environment information in tele-operation task for robots . In the proposed method , following procedures run in parallel ; 3 dominant orthogonal axis estimation and point cloud grouping by normal vectors based on the Manhattan - world assumption , fast registration using dominant axis grouped point cloud , plane position estimation for each dominant axis group , and face set generation by shape estimation for each plane . Experimental results shows that accuracy of plane position estimation is equivalent to measurement accuracy , registration takes about 0.1[s ] for each frame , and storage size is reduced to about 10 - 20 % from original 3D point cloud size .
RegCGAN is based on the conditional GAN , and we introduce two regularizers to guide the model to learn the corresponding semantics of different domains . We evaluate the proposed model on several tasks for which paired training data is not given , including the generation of edges and photos , the generation of faces with different attributes , etc . The experimental results show that our model can successfully generate corresponding images for all these tasks , while outperforms the baseline methods .
In this paper , we propose a novel multi-component deep convolutional neural network ( CNN ) based architecture which performs the complex task of makeup removal from a disguised face , to reveal the original mugshot image of the impostor ( i.e. without makeup ) . The proposed network also performs the hard tasks of FR on a disguised face in addition to recognition of identity and generation of the face of the spoofed target , by minimizing a novel multi-component objective function . Comparison of performance with a few recent state-of-the-art methods of FR over three benchmark datasets reveals the superiority of our proposed method for both synthesis as well as recognition ( FR ) tasks .
In this paper , we propose to incorporate the face-based age recognition technology to ticket inspection to solve this kind of problem , which could also greatly improve the efficiency of ticket checking . In this paper , we try to fine tuning the Inception ResnetV2 model , by replacing the original simple convolutional neural network for the facial feature extraction and recognition . The experiment results suggest that the accuracy of age recognition has been significantly improved .
Empirical analysis of cascade deformable models for multi-view face detection Robust FEC-CNN: A High Accuracy Facial Landmark Detection System Facial Landmark Extraction Scheme Based on Semantic Segmentation Dispersion assessment in the location of facial landmarks on photographs Deep neural networks regularization for structured output prediction Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques A discriminative parts based model approach for fiducial points free and shape constrained head pose normalisation in the wild Dynamic facial landmarking selection for emotion recognition using Gaussian processes Fine-Grained Head Pose Estimation Without Keypoints Nonlinear 3D Face Morphable Model Quantized Densely Connected U-Nets for Efficient Landmark Localization Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Deep Multi-Center Learning for Face Alignment FacePoseNet: Making a Case for Landmark-Free Face Alignment StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation Template adaptation for face verification and identification Local Operators and Measures for Heterogeneous Face Recognition Heterogeneous Face Recognition Using Domain Specific Units Supervised Filter Learning for Representation Based Face Recognition Parallel-Structure-based Transfer Learning for Deep NIR-to-VIS Face Recognition Local feature based multi-view discriminant analysis Ensemble based extreme learning machine for cross-modality face matching Heterogeneous Face Recognition: A Common Encoding Feature Discriminant Approach Heterogeneous Face Identification by Fusion of Local Descriptors Coupled Discriminative Feature Learning for Heterogeneous Face Recognition Feature and keypoint selection for visible to near-infrared face matching Composite Sketch Recognition Using Multi-scale Hog Features and Semantic Attributes Mutual conversion method of visible light and near-infrared human face images Person re-identification by Local Maximal Occurrence representation and metric learning Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset Multi-channel face reconstruction system based on sketch features using Conditional Adversarial Networks Realistic Face Image Generation Based on Generative Adversarial Network Talking Face Generation by Adversarially Disentangled Audio-Visual Representation Mining Audio, Text and Visual Information for Talking Face Generation Local and Global Sparsity for Deep Learning Networks Incremental learning for the detection and classification of GAN-generated images Research of 3D face reconstruction based on one image Overview of Research on 3D Face Modeling A method of 3D model generation of indoor environment with Manhattan world assumption using 3D camera Unpaired Multi-Domain Image Generation via Regularized Conditional GANs SpoofNET: Resolving Facial Makeup based Spoofs Using Inception-Resnet V2 for Face-based Age Recognition in Scenic Spots
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CVPR Workshops ANT/SEIT WACV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ECCV 2017 IEEE International Conference on Computer Vision (ICCV) Neurocomputing 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Image Vis. Comput. Face Recognition Across the Imaging Spectrum ICIG (1) BTAS CW 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IJCNN ICDM ICIG (2) SII ICVGIP CCIS
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