In this paper , an image-based age-group classification method is proposed to estimate three levels of age groups , namely child , adult and the elderly . After face detection from the acquired image frame , human facial area is extracted and 52 feature points are located by using Lucas-Kanade image alignment method . These feature points and corresponding located facial area are used to build an active appearance model ( AAM ) . For simplification object of interest such as the face can be assumed to be flat . The method was tested for face detection problem . A face detector was trained using 4916 face images and rotated for out-of-plane detection . This task is trivial for humans , but is computationally expensive for machines . Most face detection systems simplify the face detection problem by constraining the problem to frontal views of non-rotated faces . Approaches have been demonstrated capable of relaxing these constraints , at the cost of additional computation . Preprocessing of the input images is concentrated on the removal of different types of noise while preserving the phase data . For the face detection process , firstly , skin pixels are modeled by using supervised training , and at the run time , the skin pixels are detected using optimal boundary conditions . Then by finding the eye location in the skin region , human face will be extracted . Face detection has been an attractive field of research for both neuroscientists and computer vision scientists . Humans are able to identify reliably a large number of faces and neuroscientists are interested in understanding the perceptual and cognitive mechanisms at the base of the face detection process . Face detection helps human being to focus on the face recognition system , optimizing the system speed and performance . In addition to using single threshold of frame difference , we use the sequence of faces number we detected in every frame of the video to get a summary of semantic meaning of the video . But the classifier we used in face detecting is not good enough . And because of its false detection and miss detection , we got too many false key frames . This paper proposed a face detector using network-based services for a remote robot application . To perform an efficient face detecting in a real robot , a face detector needs to light computation cost and low false detection . The proposed face detector has been implemented as a network-based service so that it can be deployed either in any computer nodes in network environments or in the robot itself . This paper studies a system of face detection and recognition system based on real-time video . In the face detection part , proposed the Adaboost-ASM face detection algorithm , this algorithm is Adaboost face detection algorithm combined with the Active Shape Model , mainly to solve the problem of the Adaboost face detection algorithm is easy to false drop face in complex region and a similar face region , implement face real-time detection and eliminate non-face region . In the part of face recognition , this paper using 2D-Gabor wavelet feature extraction , and analyzes the advantage and disadvantages of PCA method and Fisher linear discriminant method , and decide using Fisherface method combined with the PCA and Fisher linear discriminant analysis method to reduce the face feature , and then do the projection of the best classification for facial feature . Therefore , this paper designs a face recognition system based on deep convolutional neural network and builds a real-time face recognition platform . The face detection part adopts a cascade structure of lab classifiers adapted to different head poses . Feature localization adopts a strategy of stepwise optimization of face alignment results following face image resolution . The experiment indicate This network convergence rate is not only quick , moreover easy to achieve the optimal solution . This network has the high detection precision to the face image detection . Method Based on Light Absorbing and Reflecting Properties for Preventing Face Image Detection . A method is proposed for preventing unauthorized face image revelation through unintentional capture of facial images . Face Detection Analysis has been an active research area in Computer Vision and Image Processing over decades and it is fundamental to many facial processing applications . Consequently , real-time Face Detection and Tracking , for the purpose of facial recognition , has remained an important research area in many human-computer interaction application scenarios . The present invention relates to a method and apparatus for obtaining search results , it belongs to a Internet art . In the method , the step that the mobile station acquire , by performing the face detection information if the current user 's face is detected that the trigger event search ; By the information to be searched in the face information , the search event , the method comprising : transmitting a search request to the search server ; Wherein the search server includes a step of receiving the search information related to the said user determined by the search by the information to the search and the information face . One transmits the information when sikyeoteul trigger a search events throughout the terminal to search for a face information to the search server proceed to login and search , and finally the search server to search by content to be searched and the face information to confirm you and It receives relevant search results . Function modules of the system implementation including human face image acquisition , image preprocessing module , face detection and orientation , face feature extraction and recognition , human face database management , etc . Face detection of system based on the characteristics of Haar-like Haar classifier , and face recognition uses Eigenfacae algorithm . After test the system , the results show that the system can meet the demand of the general authentication based on face recognition . It is widely used in recent years , face detection and face tracking has not only limited to the scope of application of face recognition : in video retrieval , video surveillance , facial expression analysis , gender , race , age discrimination , digital entertainment , and so on . This paper proposed algorithm based on AdaBoost algorithm AAM model of face feature points to identify the improvement in a certain range to solve the present stage AAM algorithm does not consider the grayscale in the exact face of initial position and face face detection problem . This compact representation is used for recognition . One naive approach would be representing a video face as a set of frame - level face features such as those extracted from deep neural networks [ reference ][ reference ] , which have dominated face recognition recently [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Such a representation comprehensively maintains the information across all frames . However , to compare two video faces , one needs to fuse the matching results across all pairs of frames between the two face videos . document : Detecting Faces Using Region - based Fully Convolutional Networks Face detection has achieved great success using the region - based methods . In this report , we propose a region - based face detector applying deep networks in a fully convolutional fashion , named Face R - FCN . The CNN - based detectors have achieved the highest performance . construct cascaded CNNs to learn face detectors with a coarse - to - fine strategy . MTCNN develops a multi - task training framework to jointly learn the face detection and alignment . Face detection is a fundamental problem in vision task . In this technical report , we propose a powerful face detection approach named Face R - FCN by integrating R - FCN and several sophisticated techniques for better detecting faces and boosting overall performance . By reasoning the drawbacks of R - CNN and R - FCN , we explore the details and invent new designs to improve the popular detection framework specifically for face detection . The whole system can be trained end - to - end in a unified CNN framework . Experiments demonstrate that our proposed algorithm is superior against state - of - the - art methods on face detection benchmarks and achieves comparable results for generic proposal generation . The source code of our system is available . . Face detection has received extensive research attention . With the emergence of modern CNN and object detector , there are many face detectors proposed to achieve promising performances , by adapting general object detection framework into face detection domain . We briefly review hard example mining , face detection architecture , and anchor design & matching . The improvement on the recall of these occluded cases usually brings the risk of high false positives . In this paper , we present a novel face detector called Face Attention Network ( FAN ) , which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed . More specifically , we propose a new anchor - level attention , which will highlight the features from the face region . Secondly we present a novel 3D-based facial expression analysis algorithm to overcome the disadvantages of the traditional 2D-based analysis , which is incapable of handling large pose variations . 3D modeling techniques have n't been used on 3D facial expression recognition due to the lack of a publicly available 3D facial expression database . Thus we developed a 3D static facial expression database , including both prototypical 3D facial expression shapes and 2D facial textures of 2,500 static models from 100 subjects . This paper studies exaggerated facial shapes in addition to original facial shapes to assist 3D Facial Expression Recognition ( FER ) . We propose a Poisson equation based approach to exaggerate facial shape characteristics to highlight expression clues that are latent in original facial surfaces but useful for recognizing expressions . Our motivation for a fusion information step is that the data issues from matchers based on different approaches can be completed and determined . Then we propose in this paper a new method of 3D facial recognition based on information fusion . Firstly we describe the different algorithms ( matchers ) used for face recognition and results issues from each algorithm . For some authentication methods , the amount of answers were satisfying , but for other less popular authentication methods the amount of answers were too low to really draw any meaningful data that could also be applied to a larger population . The best authentication method when considering both security and user-friendliness was found to be 3D facial recognition , with it having a very low false accept rate and high user-friendliness score . Three presentation attacks were also conducted during the thesis , two on fingerprint recognition and one on 2D facial recognition . 3D facial landmark detection is a crucial step for many computer vision applications , such as 3D facial expression analysis , 3D face recognition , and 3D reconstruction . Pose variations , expression changes and self-occlusion yet make 3D facial landmark detection a very challenging task . Dynamic texture analysis involves short term facial expression movements in 3D in a temporal or spatial domain . Two feature extraction algorithms are used in 3D facial expression analysis namely holistic and local algorithms . Holistic algorithms analyze the whole face whilst the local algorithms analyze a facial image in small components namely nose , mouth , cheek and forehead . In this paper , we explore bilinear models for jointly addressing 3D face and facial expression recognition . An elastically deformable model algorithm that establishes correspondence among a set of faces is proposed first and then bilinear models that decouple the identity and facial expression factors are constructed . Evaluated with the Bosphorus 3D database , the system has achieved the best performance of 0.071 overall Mean Squared Error ( MSE ) for AU intensity estimation using Support Vector Regressors , and 92.2 % average accuracy for the recognition of the six basic emotions using the proposed ensemble classifiers . In comparison with other related work , our research outperforms other state-of-the-art research on 3D facial emotion recognition for the Bosphorus database . Moreover , in on-line real-time evaluation with real human subjects , the proposed system also shows superior real-time performance with 84 % recognition accuracy and great flexibility and adaptation for newly arrived novel ( e.g. ' contempt ' which is not included in the six basic emotions ) emotion detection . We provide extensive evidence showing that models based on this convolution operator are easier to train , and can efficiently learn invariant shape features . Specifically , we evaluate our method on three different types of tasks of dense shape correspondence , 3D facial expression classification , and 3D shape reconstruction , and show that it significantly outperforms state-of-the-art approaches while being significantly faster , without relying on shape descriptors . The paper presents a novel method for 3D facial shape recognition . Our inputs are 3D facial shapes which are reconstructed from point clouds , and then filtered using PCA . Second , a supervised disentanglement splits the low-dimensional embedding vector into four sub-vectors , each of which contains separated information about one of four major face attributes ( pose , identity , expression , and style ) that can be used both for discriminative tasks and for manipulating all four attributes in an explicit manner . The resulting architecture achieves state-of-the-art image quality , good discrimination and face retrieval results on each of the four attributes , and supports various face editing tasks using a face representation of only 99 dimensions . Finally , we apply the architecture 's robust image synthesis capabilities to visually debug label-quality issues in an existing face dataset . In extension to our paper Analysis of Human Faces using a Measurement-Based Skin Reflectance Model we describe the almost fully automated face processing pipeline including the custom-built acquisition system , calibration procedures , and rendering . The underlying model is intuitive , physically plausible , and allows easy face editing . Generated image quality approaches that of real photographs . Face age editing has become a crucial task in film post-production , and is also becoming popular for general purpose photography . Recently , adversarial training has produced some of the most visually impressive results for image manipulation , including the face aging/de-aging task . The user can create and edit facial expressions by drawing freeform strokes , or by directly dragging facial points in 2D screen space . Designing such an interface for face modeling and editing is challenging because many unnatural facial expressions might be consistent with the ambiguous user input . The system automatically learns a model prior from a prerecorded facial expression database and uses it to remove the ambiguity . subsection : Extension to face hallucination Besides facial attribute transfer , our DIAT can also be extended to other face editing tasks . Here we use the face hallucination as an example .
In this paper , an image-based age-group classification method is proposed to estimate three levels of age groups , namely child , adult and the elderly . After face detection from the acquired image frame , human facial area is extracted and 52 feature points are located by using Lucas-Kanade image alignment method . These feature points and corresponding located facial area are used to build an active appearance model ( AAM ) .
For simplification object of interest such as the face can be assumed to be flat . The method was tested for face detection problem . A face detector was trained using 4916 face images and rotated for out-of-plane detection .
This task is trivial for humans , but is computationally expensive for machines . Most face detection systems simplify the face detection problem by constraining the problem to frontal views of non-rotated faces . Approaches have been demonstrated capable of relaxing these constraints , at the cost of additional computation .
Preprocessing of the input images is concentrated on the removal of different types of noise while preserving the phase data . For the face detection process , firstly , skin pixels are modeled by using supervised training , and at the run time , the skin pixels are detected using optimal boundary conditions . Then by finding the eye location in the skin region , human face will be extracted .
Face detection has been an attractive field of research for both neuroscientists and computer vision scientists . Humans are able to identify reliably a large number of faces and neuroscientists are interested in understanding the perceptual and cognitive mechanisms at the base of the face detection process . Face detection helps human being to focus on the face recognition system , optimizing the system speed and performance .
In addition to using single threshold of frame difference , we use the sequence of faces number we detected in every frame of the video to get a summary of semantic meaning of the video . But the classifier we used in face detecting is not good enough . And because of its false detection and miss detection , we got too many false key frames .
This paper proposed a face detector using network-based services for a remote robot application . To perform an efficient face detecting in a real robot , a face detector needs to light computation cost and low false detection . The proposed face detector has been implemented as a network-based service so that it can be deployed either in any computer nodes in network environments or in the robot itself .
This paper studies a system of face detection and recognition system based on real-time video . In the face detection part , proposed the Adaboost-ASM face detection algorithm , this algorithm is Adaboost face detection algorithm combined with the Active Shape Model , mainly to solve the problem of the Adaboost face detection algorithm is easy to false drop face in complex region and a similar face region , implement face real-time detection and eliminate non-face region . In the part of face recognition , this paper using 2D-Gabor wavelet feature extraction , and analyzes the advantage and disadvantages of PCA method and Fisher linear discriminant method , and decide using Fisherface method combined with the PCA and Fisher linear discriminant analysis method to reduce the face feature , and then do the projection of the best classification for facial feature .
Therefore , this paper designs a face recognition system based on deep convolutional neural network and builds a real-time face recognition platform . The face detection part adopts a cascade structure of lab classifiers adapted to different head poses . Feature localization adopts a strategy of stepwise optimization of face alignment results following face image resolution .
The experiment indicate This network convergence rate is not only quick , moreover easy to achieve the optimal solution . This network has the high detection precision to the face image detection .
Method Based on Light Absorbing and Reflecting Properties for Preventing Face Image Detection . A method is proposed for preventing unauthorized face image revelation through unintentional capture of facial images .
Face Detection Analysis has been an active research area in Computer Vision and Image Processing over decades and it is fundamental to many facial processing applications . Consequently , real-time Face Detection and Tracking , for the purpose of facial recognition , has remained an important research area in many human-computer interaction application scenarios .
The present invention relates to a method and apparatus for obtaining search results , it belongs to a Internet art . In the method , the step that the mobile station acquire , by performing the face detection information if the current user 's face is detected that the trigger event search ; By the information to be searched in the face information , the search event , the method comprising : transmitting a search request to the search server ; Wherein the search server includes a step of receiving the search information related to the said user determined by the search by the information to the search and the information face . One transmits the information when sikyeoteul trigger a search events throughout the terminal to search for a face information to the search server proceed to login and search , and finally the search server to search by content to be searched and the face information to confirm you and It receives relevant search results .
Function modules of the system implementation including human face image acquisition , image preprocessing module , face detection and orientation , face feature extraction and recognition , human face database management , etc . Face detection of system based on the characteristics of Haar-like Haar classifier , and face recognition uses Eigenfacae algorithm . After test the system , the results show that the system can meet the demand of the general authentication based on face recognition .
It is widely used in recent years , face detection and face tracking has not only limited to the scope of application of face recognition : in video retrieval , video surveillance , facial expression analysis , gender , race , age discrimination , digital entertainment , and so on . This paper proposed algorithm based on AdaBoost algorithm AAM model of face feature points to identify the improvement in a certain range to solve the present stage AAM algorithm does not consider the grayscale in the exact face of initial position and face face detection problem .
This compact representation is used for recognition . One naive approach would be representing a video face as a set of frame - level face features such as those extracted from deep neural networks [ reference ][ reference ] , which have dominated face recognition recently [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Such a representation comprehensively maintains the information across all frames . However , to compare two video faces , one needs to fuse the matching results across all pairs of frames between the two face videos .
document : Detecting Faces Using Region - based Fully Convolutional Networks Face detection has achieved great success using the region - based methods . In this report , we propose a region - based face detector applying deep networks in a fully convolutional fashion , named Face R - FCN .
The CNN - based detectors have achieved the highest performance . construct cascaded CNNs to learn face detectors with a coarse - to - fine strategy . MTCNN develops a multi - task training framework to jointly learn the face detection and alignment .
Face detection is a fundamental problem in vision task . In this technical report , we propose a powerful face detection approach named Face R - FCN by integrating R - FCN and several sophisticated techniques for better detecting faces and boosting overall performance . By reasoning the drawbacks of R - CNN and R - FCN , we explore the details and invent new designs to improve the popular detection framework specifically for face detection .
The whole system can be trained end - to - end in a unified CNN framework . Experiments demonstrate that our proposed algorithm is superior against state - of - the - art methods on face detection benchmarks and achieves comparable results for generic proposal generation . The source code of our system is available . .
Face detection has received extensive research attention . With the emergence of modern CNN and object detector , there are many face detectors proposed to achieve promising performances , by adapting general object detection framework into face detection domain . We briefly review hard example mining , face detection architecture , and anchor design & matching .
The improvement on the recall of these occluded cases usually brings the risk of high false positives . In this paper , we present a novel face detector called Face Attention Network ( FAN ) , which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed . More specifically , we propose a new anchor - level attention , which will highlight the features from the face region .
Secondly we present a novel 3D-based facial expression analysis algorithm to overcome the disadvantages of the traditional 2D-based analysis , which is incapable of handling large pose variations . 3D modeling techniques have n't been used on 3D facial expression recognition due to the lack of a publicly available 3D facial expression database . Thus we developed a 3D static facial expression database , including both prototypical 3D facial expression shapes and 2D facial textures of 2,500 static models from 100 subjects .
This paper studies exaggerated facial shapes in addition to original facial shapes to assist 3D Facial Expression Recognition ( FER ) . We propose a Poisson equation based approach to exaggerate facial shape characteristics to highlight expression clues that are latent in original facial surfaces but useful for recognizing expressions .
Our motivation for a fusion information step is that the data issues from matchers based on different approaches can be completed and determined . Then we propose in this paper a new method of 3D facial recognition based on information fusion . Firstly we describe the different algorithms ( matchers ) used for face recognition and results issues from each algorithm .
For some authentication methods , the amount of answers were satisfying , but for other less popular authentication methods the amount of answers were too low to really draw any meaningful data that could also be applied to a larger population . The best authentication method when considering both security and user-friendliness was found to be 3D facial recognition , with it having a very low false accept rate and high user-friendliness score . Three presentation attacks were also conducted during the thesis , two on fingerprint recognition and one on 2D facial recognition .
3D facial landmark detection is a crucial step for many computer vision applications , such as 3D facial expression analysis , 3D face recognition , and 3D reconstruction . Pose variations , expression changes and self-occlusion yet make 3D facial landmark detection a very challenging task .
Dynamic texture analysis involves short term facial expression movements in 3D in a temporal or spatial domain . Two feature extraction algorithms are used in 3D facial expression analysis namely holistic and local algorithms . Holistic algorithms analyze the whole face whilst the local algorithms analyze a facial image in small components namely nose , mouth , cheek and forehead .
In this paper , we explore bilinear models for jointly addressing 3D face and facial expression recognition . An elastically deformable model algorithm that establishes correspondence among a set of faces is proposed first and then bilinear models that decouple the identity and facial expression factors are constructed .
Evaluated with the Bosphorus 3D database , the system has achieved the best performance of 0.071 overall Mean Squared Error ( MSE ) for AU intensity estimation using Support Vector Regressors , and 92.2 % average accuracy for the recognition of the six basic emotions using the proposed ensemble classifiers . In comparison with other related work , our research outperforms other state-of-the-art research on 3D facial emotion recognition for the Bosphorus database . Moreover , in on-line real-time evaluation with real human subjects , the proposed system also shows superior real-time performance with 84 % recognition accuracy and great flexibility and adaptation for newly arrived novel ( e.g. ' contempt ' which is not included in the six basic emotions ) emotion detection .
We provide extensive evidence showing that models based on this convolution operator are easier to train , and can efficiently learn invariant shape features . Specifically , we evaluate our method on three different types of tasks of dense shape correspondence , 3D facial expression classification , and 3D shape reconstruction , and show that it significantly outperforms state-of-the-art approaches while being significantly faster , without relying on shape descriptors .
The paper presents a novel method for 3D facial shape recognition . Our inputs are 3D facial shapes which are reconstructed from point clouds , and then filtered using PCA .
Second , a supervised disentanglement splits the low-dimensional embedding vector into four sub-vectors , each of which contains separated information about one of four major face attributes ( pose , identity , expression , and style ) that can be used both for discriminative tasks and for manipulating all four attributes in an explicit manner . The resulting architecture achieves state-of-the-art image quality , good discrimination and face retrieval results on each of the four attributes , and supports various face editing tasks using a face representation of only 99 dimensions . Finally , we apply the architecture 's robust image synthesis capabilities to visually debug label-quality issues in an existing face dataset .
In extension to our paper Analysis of Human Faces using a Measurement-Based Skin Reflectance Model we describe the almost fully automated face processing pipeline including the custom-built acquisition system , calibration procedures , and rendering . The underlying model is intuitive , physically plausible , and allows easy face editing . Generated image quality approaches that of real photographs .
Face age editing has become a crucial task in film post-production , and is also becoming popular for general purpose photography . Recently , adversarial training has produced some of the most visually impressive results for image manipulation , including the face aging/de-aging task .
The user can create and edit facial expressions by drawing freeform strokes , or by directly dragging facial points in 2D screen space . Designing such an interface for face modeling and editing is challenging because many unnatural facial expressions might be consistent with the ambiguous user input . The system automatically learns a model prior from a prerecorded facial expression database and uses it to remove the ambiguity .
subsection : Extension to face hallucination Besides facial attribute transfer , our DIAT can also be extended to other face editing tasks . Here we use the face hallucination as an example .
Image-based age-group classification design using facial features Out-of-Plane Rotated Object Detection using Patch Feature based Classifier Hardware Implementation of Face Detection Using ADABOOST Algorithm Real-Time Human Face Detection in Noisy Images Based on Skin Color Fusion Model and Eye Detection Gabor feature extraction for gray scale image Face Detection-based Video Key Frame Extraction Face Detector using Network-based Services for a Remote Robot Application An Exploration on Data Mining for Face Detection based on Real-time Face Tracking Design of Distance Education Authentication System Based on Deep Convolutional Neural Netework Face Detection Based on GA-BP Neural Network Privacy Visor: Method Based on Light Absorbing and Reflecting Properties for Preventing Face Image Detection Infrared-Modified V-Gear Talk-Cam Tracer for Image Processing Method and apparatus for acquiring search results Design and implementation of C++ and OpenCV-based face recognition system Face Feature Points Detection Based on Adaboost and AAM Neural Aggregation Network for Video Face Recognition Detecting Faces Using Region-based Fully Convolutional Networks Recurrent Scale Approximation for Object Detection in CNN Robust Face Detection via Learning Small Faces on Hard Images Face Attention Network: An Effective Face Detector for the Occluded Faces Three-dimensional facial expression modeling and analysis with topographic information Enhancing 3D Facial Expression Recognition by Exaggerating Geometry Characteristics 3D face recognition using multi matcher approach An evaluation of authentication methods for solutions that require a high degree of both security and user-friendliness on mobile phones Deep 3D Facial Landmark Detection on Position Maps Facial Expression Detection for video sequences using local feature extraction algorithms Bilinear Models for 3-D Face and Facial Expression Recognition Adaptive 3D facial action intensity estimation and emotion recognition SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator Geometric Modeling and Processing - GMP 2006, 4th International Conference, Pittsburgh, PA, USA, July 26-28, 2006, Proceedings Face Recognition Using Simplicial Complexes Processing and editing of faces using a measurement-based skin reflectance model High Resolution Face Age Editing Face poser: interactive modeling of 3D facial expressions using model priors Deep Identity-aware Transfer of Facial Attributes
f02c2c131cca76f21da600b6b81c485a4f7869eb 732d34564bdbd0ca309b4d66dbb851f3f489f195 07bf670e5418d6605c8f43b3b479de2974dc07e6 73aa8294ee6d5b63547410ac9eb51042f1118832 732e786b7db3517a8a2500a2a626c763f1db4157 d937c0e6309895723da2c4e84f7f2d62f1026eec 2d0e71836c66fd09143d7edbf8bfd61b1ccc12f0 7c71ee74f9b389493d9c8c4b6cb9098c71f2fd9c 4425ea826207e1e0266ca26968edab2b9d29a3c2 a96ac0de1a3995d8ec8dc8aa68dbcaa616cc2ee0 5e641b0d9ae82df7f565e245758a1c11892c33ce 8ccda0885dc40b366f7e1ed75e59a487d25b615d 860308940b87436c6fea7dee3a310ac01cc7003a 01a05fc3eeb36bfb1669e6aabbeaa02f31836243 562f00b70269150a29059585b1e7de8bee10316c f1a2e707b488742da6baa2d05e58e5a13f110386 0ede355c5e71a2a7146a8d19fe1f20d38d3d13ad 7e6e4ccc1d9dc8ff4f60d25e6561c4c395c27671 31595057b328199ab8bdcfa058d1486c5f658347 ed6ff7213cd95b7f9bf18688e29f6cc3a6ed5b2b d60491c2e5ddc8b7fdba9b7f06d413db72ec9bd5 1068f6eca07c35426ca67961f00c3cac4866f155 e4ceaa284dcf9a9ac9d58d953f592bd71319ffe9 5969da915e121e2e2001e119f5962a1c413e44c1 dc26e09025457d15fe4f42e6de18cbf56b62d9ea ff6af78662e2ca6b0d6c692b7de240dc0bd5b07a 5bdeb0835726a6d54f8a150d5aa54d69892240dd c5727a6e0b19cc2cbb2fc8341856c65e89654ff2 fd3d73040e8e5463ced4ea4fc46a482574decd62
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2010 2012 2012 2015 2011 2013 2012 2014 2018 2008 2013 2012 2015 2014 2016 2017 2017 2017 2020 2017 2008 2017 2011 2019 2019 2019 2008 2015 2019 2006 2010 2006 2020 2007 2016
ICWET SMC GRMSE (1) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2017 IEEE International Conference on Computer Vision (ICCV) 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) ArXiv CCBR IScIDE (1) ICCV Workshops MCPR SIGGRAPH Sketches Symposium on Computer Animation ArXiv
https://api.semanticscholar.org/f02c2c131cca76f21da600b6b81c485a4f7869eb https://api.semanticscholar.org/732d34564bdbd0ca309b4d66dbb851f3f489f195 https://api.semanticscholar.org/07bf670e5418d6605c8f43b3b479de2974dc07e6 https://api.semanticscholar.org/73aa8294ee6d5b63547410ac9eb51042f1118832 https://api.semanticscholar.org/732e786b7db3517a8a2500a2a626c763f1db4157 https://api.semanticscholar.org/d937c0e6309895723da2c4e84f7f2d62f1026eec https://api.semanticscholar.org/2d0e71836c66fd09143d7edbf8bfd61b1ccc12f0 https://api.semanticscholar.org/7c71ee74f9b389493d9c8c4b6cb9098c71f2fd9c https://api.semanticscholar.org/4425ea826207e1e0266ca26968edab2b9d29a3c2 https://api.semanticscholar.org/a96ac0de1a3995d8ec8dc8aa68dbcaa616cc2ee0 https://api.semanticscholar.org/5e641b0d9ae82df7f565e245758a1c11892c33ce https://api.semanticscholar.org/8ccda0885dc40b366f7e1ed75e59a487d25b615d https://api.semanticscholar.org/860308940b87436c6fea7dee3a310ac01cc7003a https://api.semanticscholar.org/01a05fc3eeb36bfb1669e6aabbeaa02f31836243 https://api.semanticscholar.org/562f00b70269150a29059585b1e7de8bee10316c https://www.semanticscholar.org/paper/59fb2a2898decc1d2f8f6259af8036500c95c422 https://www.semanticscholar.org/paper/2e942d19333651bf6012374ea9e78d6937fd33ac https://www.semanticscholar.org/paper/28704b0b48948f538cd346a4296ebde5bdba1efb https://www.semanticscholar.org/paper/25a784f7f8c94c42821ee078587fc38dffcd00a4 https://www.semanticscholar.org/paper/3600aac8edc5bc015e69f2ffa893c21b6d4e1057 https://api.semanticscholar.org/f1a2e707b488742da6baa2d05e58e5a13f110386 https://api.semanticscholar.org/0ede355c5e71a2a7146a8d19fe1f20d38d3d13ad https://api.semanticscholar.org/7e6e4ccc1d9dc8ff4f60d25e6561c4c395c27671 https://api.semanticscholar.org/31595057b328199ab8bdcfa058d1486c5f658347 https://api.semanticscholar.org/ed6ff7213cd95b7f9bf18688e29f6cc3a6ed5b2b https://api.semanticscholar.org/d60491c2e5ddc8b7fdba9b7f06d413db72ec9bd5 https://api.semanticscholar.org/1068f6eca07c35426ca67961f00c3cac4866f155 https://api.semanticscholar.org/e4ceaa284dcf9a9ac9d58d953f592bd71319ffe9 https://api.semanticscholar.org/5969da915e121e2e2001e119f5962a1c413e44c1 https://api.semanticscholar.org/dc26e09025457d15fe4f42e6de18cbf56b62d9ea https://api.semanticscholar.org/ff6af78662e2ca6b0d6c692b7de240dc0bd5b07a https://api.semanticscholar.org/5bdeb0835726a6d54f8a150d5aa54d69892240dd https://api.semanticscholar.org/c5727a6e0b19cc2cbb2fc8341856c65e89654ff2 https://api.semanticscholar.org/fd3d73040e8e5463ced4ea4fc46a482574decd62 https://www.semanticscholar.org/paper/2b507f659b341ed0f23106446de8e4322f4a3f7e