However , it is not appropriate to represent the similarity of two image patches merely according to image intensities , especially under uncontrolled environments such as varying illumination conditions . In this paper , we propose a multi-view representation of image patches for face sketch synthesis . It first constructs a high dimensional multi-view feature vector through a hierarchical framework including multiple filters and multiple local features . In this paper , presents an unsupervised method for face photo- sketch recognition . The face photo- sketch synthesis has two main steps . One is edge detection for only recognition of face and secondly is for hair detection . To tackle this challenge , in this article , we propose using facial composition information to help the synthesis of face sketch/photo . Especially , we propose a novel composition-aided generative adversarial network ( CA-GAN ) for face photo-sketch synthesis . In CA-GAN , we utilize paired inputs , including a face photo/sketch and the corresponding pixelwise face labels for generating a sketch/photo . Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry . Despite the significant improvements in sketch-to-photo synthesis techniques , existing methods have still serious limitations in practice , such as the need for paired data in the training phase or having no control on enforcing facial attributes over the synthesized image . Automatic face sketch synthesis has important applications in law enforcement and digital entertainment . Although great progress has been made in recent years , previous methods only work under well controlled conditions and often fail when there are variations of lighting and pose . Automatic face sketch synthesis has been widely applied in digital entertainment and law enforcement . Currently , most sketch synthesis algorithms focus on generating face portrait of good quality , but ignoring the time consumption . Face sketch synthesis and recognition have wide range of applications in law enforcement . Despite the impressive progresses have been made in faces sketch and recognition , most existing researches regard them as two separate tasks . In this paper , we propose a generative adversarial multitask learning method in order to deal with face sketch synthesis and recognition simultaneously . Our framework is based on generative adversarial networks ( GAN ) , in which an improved deep network named residual dense U-Net is used as generator to synthesize face sketch image and a multi-task discriminator is designed to not only guide the generator to produce more realistic sketch image , but also extract discriminative face feature . Despite this , the most widely adopted perceptual metrics today , such as SSIM and FSIM , are simple , shallow functions , and fail to consider many factors of human perception . Recently , the facial modeling community has observed that the inclusion of both structure and texture has a significant positive benefit for face sketch synthesis ( FSS ) . But how perceptual are these so-called “ perceptual features ” ? Abstract Face sketch synthesis refers to the technique generating a sketch from an input photo . Existing methods are data-driven , which synthesize a sketch by linearly combining K candidate sketch patches which are purposely selected from the training data . Article proposed novel method of automatic sketch synthesis , that can be used for creating test databases and sketch recognition tasks research and retrieval of corresponding photo . These methods were applied to two popular benchmark face databases . Recent researches of feature maps , which are the outputs of a certain neural network layer , have achieved great success in texture synthesis and artistic image generation . In this paper , we reformulate the face sketch synthesis problem into a neural network feature maps based optimization task . Our results accurately capture the sketch drawing style and make full use of the whole stylistic information hidden in the training dataset . Generative Adversarial Networks . Generative adversarial networks ( GANs ) have shown remarkable results in various computer vision tasks such as image generation , image translation , super - resolution imaging , and face image synthesis . A typical GAN model consists of two modules : a discriminator and a generator . Evaluations demonstrate that SA-CPD mitigates tangential sliding problem in CPD and outperforms other competing shape morphing methods , in some cases substantially . 3D morphable models are constructed from over 1200 full head scans , and we evaluate the constructed models in terms of age and gender classification . The best performance , in the context of SVM classification , is achieved using the proposed SA-CPD deformation algorithm . Each one of them is different in their architecture which in turn influences the accuracy of the results . In this survey , we compare the state of the art CNN algorithms which perform each of these tasks , namely , face detection , age and gender classification , and ethnicity classification . We compare these algorithms with respect to their performance and accuracy so that an appropriate algorithm can be selected for the above application . The geometric features of facial images like eyes , nose , mouth etc . are located by using Feature extraction algorithm and face recognition is performed . Based on the texture and shape information , gender and age classification is done by comparing histogram of the query image and the histogram of the images in dataset respectively . By using the proposed work , ratio of 100 % for face matching , 90 % for gender classification , and 85 % for age classification can be achieved . Hence monitoring the crowded event requires an intelligent sophisticated technology with human gender classification and classify age group . This paper is using hyper-parameter optimization and SDN controller on age gender classification on events cameras to monitor and classify to cover the whole event . Many cameras will need and lead to weak performance in classification . Several hyperparameter optimization methods were compared by configuring DNNs for character recognition and age/gender classification . Numerical results demonstrated that the Nelder-Mead method outperforms the other methods and achieves state-of-the-art accuracy for age/gender classification . Technically , our network is pretrained on an IMDb-WIKI with noisy labels and then fine-tuned on MORPH-II and finally on the training set of the OIU-Adience ( original ) dataset . The experimental results , when analyzed for classification accuracy on the same OIU-Adience benchmark , show that our model obtains the state-of-the-art performance in both age group and gender classification . It improves over the best-reported results by 16.6 % ( exact accuracy ) and 3.2 % ( one-off accuracy ) for age group classification and also there is an improvement of 3.0 % ( exact accuracy ) for gender classification . Deep BLSTM-LSTM network has been used to construct a hybrid learning framework for the aforementioned analysis . Accuracy of 93.7 % and 97.5 % have been recorded for age and gender classification problems respectively . These values are better than the state-of-the-art methods . We investigate three problem formulations with five loss functions . We propose a multi-task architecture for age , gender and race classification . We obtain state-of-the-arts age estimation results on two large benchmark datasets . This analysis paper examines the effect of using deeper convolutional neural networks trained on separate facial components and the results are compared with the state-of-the-art gender classification techniques . We also investigate the effects of network settings and parameters surrounding convolutional neural networks , how they affect the overall classification and provide insights into age-related gender classification . The results show that the proposed technique is promising and performs better with larger crop sizes . These recognition systems must apply different pattern recognition techniques , in order to distinct gender and age in the images . In this work , we propose a multi-agent system that integrates different techniques for the acquisition , preprocessing and processing of images for the classification of age and gender . The system has been tested in an office building . Although sometimes alignments involve non - parametric or part - based warps , often , global 2D or 3D ( parametric ) transformation are all that is required for this purpose . Such aligned faces are then further processed in systems for face recognition , emotion recognition , age and gender estimation , and more . In fact , it was recently claimed that a global alignment is both more robust and far faster to warp than non - parametric transformations . This paper presents a new approach for facial attribute classification using a multi-task learning approach . Unlike other approaches that uses hand engineered features , our model learns a shared feature representation that is wellsuited for multiple attribute classification . This paper proposes a method for regression facial attributes classification . We propose two simultaneous optimization problems for Facial Attribute Classification . A multilevel feature extraction method was proposed to discriminate the facial features . When the dimensionality of data vectors is high relative to the number of vectors , the dual formulation of the novel gCCA is also developed . Tests on two real datasets for facial image classification showcase the merits of the proposed approaches relative to their competing alternatives . In this paper , we propose a hierarchical graphical model that probabilistically estimates continuous head pose angles from real-world videos , by leveraging the temporal pose information over frames . The proposed graphical model is a general framework , which is able to use any type of feature and can be adapted to any facial classification task . Furthermore , the framework outputs the entire pose distribution for a given video frame . In this paper we examine cases where we believe race is one such factor . We look in particular at two forms of problem ; facial classification and image synthesis . We take the novel approach of considering race as a boundary for transfer learning in both the task ( facial classification ) and the domain ( synthesis over distinct datasets ) . It is still an open problem due to the variations of facial attributes ( e.g. , head pose , facial expression , illumination variation ) . Many studies have shown that face alignment and facial attribute analysis are often correlated . This paper develops a two-stage multi-task Auto-encoders framework for fast face alignment by incorporating head pose information to handle large view variations . Within the domains of computational geometry and computer vision are two problems : Finding large , interesting holes in high dimensional data , and locating and automatically classifying facial features from images . State of the art methods for facial feature classification are compared and new methods for finding empty hyper-rectangles are introduced . The problem of finding holes is then linked to the problem of extracting features from images and deep learning methods such as convolutional neural networks . This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images . The approach taken in this work consists of five stages , namely face detection , facial feature ( eyes , nose and mouth ) extraction , moment generation , facial feature classification and face identification . Subsequently , these stages were applied to 3065 images from three distinct facial databases , namely ORL , Yale and AR . This paper introduces the new idea of describing people using first names . We show that describing people in terms of similarity to a vector of possible first names is a powerful representation of facial appearance that can be used for a number of important applications , such as naming never-seen faces and building facial attribute classifiers . We build models for 100 common first names used in the US and for each pair , construct a pairwise first-name classifier . This paper proposes that by utilizing these correlations properly , an improvement can be achieved on the recognition of different attributes . Therefore , we propose a facial attribute recognition approach based on the grouping of different facial attribute tasks and a multi-task CNN structure . Our approach can fully utilize the correlations between attributes , and achieve a satisfactory recognition result on a large number of attributes with limited amount of parameters . The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers . Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer . The obtained results can be easily reproduced , as both the data set and source code can be downloaded . section : Related Work In the past two decades , many efforts have been devoted to visual recognition , including at least image classification , object detection , semantic segmentation , and facial attribute ( apparent age and head pose ) estimation . These works can be divided into two streams .
However , it is not appropriate to represent the similarity of two image patches merely according to image intensities , especially under uncontrolled environments such as varying illumination conditions . In this paper , we propose a multi-view representation of image patches for face sketch synthesis . It first constructs a high dimensional multi-view feature vector through a hierarchical framework including multiple filters and multiple local features .
In this paper , presents an unsupervised method for face photo- sketch recognition . The face photo- sketch synthesis has two main steps . One is edge detection for only recognition of face and secondly is for hair detection .
To tackle this challenge , in this article , we propose using facial composition information to help the synthesis of face sketch/photo . Especially , we propose a novel composition-aided generative adversarial network ( CA-GAN ) for face photo-sketch synthesis . In CA-GAN , we utilize paired inputs , including a face photo/sketch and the corresponding pixelwise face labels for generating a sketch/photo .
Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry . Despite the significant improvements in sketch-to-photo synthesis techniques , existing methods have still serious limitations in practice , such as the need for paired data in the training phase or having no control on enforcing facial attributes over the synthesized image .
Automatic face sketch synthesis has important applications in law enforcement and digital entertainment . Although great progress has been made in recent years , previous methods only work under well controlled conditions and often fail when there are variations of lighting and pose .
Automatic face sketch synthesis has been widely applied in digital entertainment and law enforcement . Currently , most sketch synthesis algorithms focus on generating face portrait of good quality , but ignoring the time consumption .
Face sketch synthesis and recognition have wide range of applications in law enforcement . Despite the impressive progresses have been made in faces sketch and recognition , most existing researches regard them as two separate tasks . In this paper , we propose a generative adversarial multitask learning method in order to deal with face sketch synthesis and recognition simultaneously . Our framework is based on generative adversarial networks ( GAN ) , in which an improved deep network named residual dense U-Net is used as generator to synthesize face sketch image and a multi-task discriminator is designed to not only guide the generator to produce more realistic sketch image , but also extract discriminative face feature .
Despite this , the most widely adopted perceptual metrics today , such as SSIM and FSIM , are simple , shallow functions , and fail to consider many factors of human perception . Recently , the facial modeling community has observed that the inclusion of both structure and texture has a significant positive benefit for face sketch synthesis ( FSS ) . But how perceptual are these so-called “ perceptual features ” ?
Abstract Face sketch synthesis refers to the technique generating a sketch from an input photo . Existing methods are data-driven , which synthesize a sketch by linearly combining K candidate sketch patches which are purposely selected from the training data .
Article proposed novel method of automatic sketch synthesis , that can be used for creating test databases and sketch recognition tasks research and retrieval of corresponding photo . These methods were applied to two popular benchmark face databases .
Recent researches of feature maps , which are the outputs of a certain neural network layer , have achieved great success in texture synthesis and artistic image generation . In this paper , we reformulate the face sketch synthesis problem into a neural network feature maps based optimization task . Our results accurately capture the sketch drawing style and make full use of the whole stylistic information hidden in the training dataset .
Generative Adversarial Networks . Generative adversarial networks ( GANs ) have shown remarkable results in various computer vision tasks such as image generation , image translation , super - resolution imaging , and face image synthesis . A typical GAN model consists of two modules : a discriminator and a generator .
Evaluations demonstrate that SA-CPD mitigates tangential sliding problem in CPD and outperforms other competing shape morphing methods , in some cases substantially . 3D morphable models are constructed from over 1200 full head scans , and we evaluate the constructed models in terms of age and gender classification . The best performance , in the context of SVM classification , is achieved using the proposed SA-CPD deformation algorithm .
Each one of them is different in their architecture which in turn influences the accuracy of the results . In this survey , we compare the state of the art CNN algorithms which perform each of these tasks , namely , face detection , age and gender classification , and ethnicity classification . We compare these algorithms with respect to their performance and accuracy so that an appropriate algorithm can be selected for the above application .
The geometric features of facial images like eyes , nose , mouth etc . are located by using Feature extraction algorithm and face recognition is performed . Based on the texture and shape information , gender and age classification is done by comparing histogram of the query image and the histogram of the images in dataset respectively . By using the proposed work , ratio of 100 % for face matching , 90 % for gender classification , and 85 % for age classification can be achieved .
Hence monitoring the crowded event requires an intelligent sophisticated technology with human gender classification and classify age group . This paper is using hyper-parameter optimization and SDN controller on age gender classification on events cameras to monitor and classify to cover the whole event . Many cameras will need and lead to weak performance in classification .
Several hyperparameter optimization methods were compared by configuring DNNs for character recognition and age/gender classification . Numerical results demonstrated that the Nelder-Mead method outperforms the other methods and achieves state-of-the-art accuracy for age/gender classification .
Technically , our network is pretrained on an IMDb-WIKI with noisy labels and then fine-tuned on MORPH-II and finally on the training set of the OIU-Adience ( original ) dataset . The experimental results , when analyzed for classification accuracy on the same OIU-Adience benchmark , show that our model obtains the state-of-the-art performance in both age group and gender classification . It improves over the best-reported results by 16.6 % ( exact accuracy ) and 3.2 % ( one-off accuracy ) for age group classification and also there is an improvement of 3.0 % ( exact accuracy ) for gender classification .
Deep BLSTM-LSTM network has been used to construct a hybrid learning framework for the aforementioned analysis . Accuracy of 93.7 % and 97.5 % have been recorded for age and gender classification problems respectively . These values are better than the state-of-the-art methods .
We investigate three problem formulations with five loss functions . We propose a multi-task architecture for age , gender and race classification . We obtain state-of-the-arts age estimation results on two large benchmark datasets .
This analysis paper examines the effect of using deeper convolutional neural networks trained on separate facial components and the results are compared with the state-of-the-art gender classification techniques . We also investigate the effects of network settings and parameters surrounding convolutional neural networks , how they affect the overall classification and provide insights into age-related gender classification . The results show that the proposed technique is promising and performs better with larger crop sizes .
These recognition systems must apply different pattern recognition techniques , in order to distinct gender and age in the images . In this work , we propose a multi-agent system that integrates different techniques for the acquisition , preprocessing and processing of images for the classification of age and gender . The system has been tested in an office building .
Although sometimes alignments involve non - parametric or part - based warps , often , global 2D or 3D ( parametric ) transformation are all that is required for this purpose . Such aligned faces are then further processed in systems for face recognition , emotion recognition , age and gender estimation , and more . In fact , it was recently claimed that a global alignment is both more robust and far faster to warp than non - parametric transformations .
This paper presents a new approach for facial attribute classification using a multi-task learning approach . Unlike other approaches that uses hand engineered features , our model learns a shared feature representation that is wellsuited for multiple attribute classification .
This paper proposes a method for regression facial attributes classification . We propose two simultaneous optimization problems for Facial Attribute Classification . A multilevel feature extraction method was proposed to discriminate the facial features .
When the dimensionality of data vectors is high relative to the number of vectors , the dual formulation of the novel gCCA is also developed . Tests on two real datasets for facial image classification showcase the merits of the proposed approaches relative to their competing alternatives .
In this paper , we propose a hierarchical graphical model that probabilistically estimates continuous head pose angles from real-world videos , by leveraging the temporal pose information over frames . The proposed graphical model is a general framework , which is able to use any type of feature and can be adapted to any facial classification task . Furthermore , the framework outputs the entire pose distribution for a given video frame .
In this paper we examine cases where we believe race is one such factor . We look in particular at two forms of problem ; facial classification and image synthesis . We take the novel approach of considering race as a boundary for transfer learning in both the task ( facial classification ) and the domain ( synthesis over distinct datasets ) .
It is still an open problem due to the variations of facial attributes ( e.g. , head pose , facial expression , illumination variation ) . Many studies have shown that face alignment and facial attribute analysis are often correlated . This paper develops a two-stage multi-task Auto-encoders framework for fast face alignment by incorporating head pose information to handle large view variations .
Within the domains of computational geometry and computer vision are two problems : Finding large , interesting holes in high dimensional data , and locating and automatically classifying facial features from images . State of the art methods for facial feature classification are compared and new methods for finding empty hyper-rectangles are introduced . The problem of finding holes is then linked to the problem of extracting features from images and deep learning methods such as convolutional neural networks .
This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images . The approach taken in this work consists of five stages , namely face detection , facial feature ( eyes , nose and mouth ) extraction , moment generation , facial feature classification and face identification . Subsequently , these stages were applied to 3065 images from three distinct facial databases , namely ORL , Yale and AR .
This paper introduces the new idea of describing people using first names . We show that describing people in terms of similarity to a vector of possible first names is a powerful representation of facial appearance that can be used for a number of important applications , such as naming never-seen faces and building facial attribute classifiers . We build models for 100 common first names used in the US and for each pair , construct a pairwise first-name classifier .
This paper proposes that by utilizing these correlations properly , an improvement can be achieved on the recognition of different attributes . Therefore , we propose a facial attribute recognition approach based on the grouping of different facial attribute tasks and a multi-task CNN structure . Our approach can fully utilize the correlations between attributes , and achieve a satisfactory recognition result on a large number of attributes with limited amount of parameters .
The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers . Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer . The obtained results can be easily reproduced , as both the data set and source code can be downloaded .
section : Related Work In the past two decades , many efforts have been devoted to visual recognition , including at least image classification , object detection , semantic segmentation , and facial attribute ( apparent age and head pose ) estimation . These works can be divided into two streams .
Multi-View Representation Based Face Sketch Synthesis Unsupervised Method for Face Photo – Sketch Synthesis and Recognition Toward Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs. Facial Attributes Guided Deep Sketch-to-Photo Synthesis Lighting and Pose Robust Face Sketch Synthesis Fast Face Sketch Synthesis via KD-Tree Search Generative Adversarial Multi-Task Learning for Face Sketch Synthesis and Recognition Scoot: A Perceptual Metric for Facial Sketches Data-driven vs. model-driven: Fast face sketch synthesis Sketch generation from photo to create test databases Deep Neural Representation Guided Face Sketch Synthesis StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation Symmetric Shape Morphing for 3D Face and Head Modelling CNN Algorithms for Detection of Human Face Attributes – A Survey An Image Mining System for Gender Classification & Age Prediction Based on Facial Features Market Intelligence Analysis on Age Estimation and Gender Classification on Events with deep learning hyperparameters optimization and SDN Controllers Effective hyperparameter optimization using Nelder-Mead method in deep learning Deeply Learned Classifiers for Age and Gender Predictions of Unfiltered Faces EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model Diagnosing deep learning models for high accuracy age estimation from a single image Facial Gender Classification — Analysis using Convolutional Neural Networks A multi-agent system for the classification of gender and age from images FacePoseNet: Making a Case for Landmark-Free Face Alignment Facial Attributes Classification Using Multi-task Representation Learning Regression Facial Attribute Classification via simultaneous dictionary learning Canonical Correlation Analysis with Common Graph Priors Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos Considering Race a Problem of Transfer Learning Fast multi-view face alignment via multi-task auto-encoders Applications of Computational Geometry and Computer Vision Multilocal feature selection using genetic algorithm for face identification The Hidden Sides of Names—Face Modeling with First Name Attributes Grouped Multi-Task CNN for Facial Attribute Recognition A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices Deep Label Distribution Learning With Label Ambiguity
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