An All-In-One Convolutional Neural Network for Face Analysis

We present a multi-purpose algorithm for simultaneousface detection, face alignment, pose estimation, genderrecognition, smile detection, age estimation and face recognitionusing a single deep convolutional neural network (CNN). Theproposed method employs a multi-task learning framework thatregularizes the shared parameters of CNN and builds a synergyamong different domains and tasks. Extensive experimentsshow that the network has a better understanding of face andachieves state-of-the-art result for most of these tasks

[1]  Junjie Yan,et al.  Face detection by structural models , 2014, Image Vis. Comput..

[2]  Jian Sun,et al.  Joint Cascade Face Detection and Alignment , 2014, ECCV.

[3]  Tal Hassner,et al.  Age and gender classification using convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[4]  Mohamed R. Amer,et al.  Facial Attributes Classification Using Multi-task Representation Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[5]  Rama Chellappa,et al.  Unconstrained face verification using deep CNN features , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[6]  Yu Qiao,et al.  Gender and Smile Classification Using Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  Jian Sun,et al.  Face Alignment by Explicit Shape Regression , 2012, International Journal of Computer Vision.

[8]  Jian-Jiun Ding,et al.  Facial age estimation based on label-sensitive learning and age-oriented regression , 2013, Pattern Recognit..

[9]  Jun-Cheng Chen,et al.  An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[10]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[11]  Hanqing Lu,et al.  DeepBE: Learning Deep Binary Encoding for Multi-label Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Luc Van Gool,et al.  DEX: Deep EXpectation of Apparent Age from a Single Image , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[13]  Shuo Yang,et al.  From Facial Parts Responses to Face Detection: A Deep Learning Approach , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Pietro Perona,et al.  Robust Face Landmark Estimation under Occlusion , 2013, 2013 IEEE International Conference on Computer Vision.

[15]  Anil K. Jain,et al.  Age estimation from face images: Human vs. machine performance , 2013, 2013 International Conference on Biometrics (ICB).

[16]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[17]  Sergio Escalera,et al.  ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[18]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[19]  Rama Chellappa,et al.  A cascaded convolutional neural network for age estimation of unconstrained faces , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[20]  Xiaoming Liu,et al.  Pose-Invariant 3D Face Alignment , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[23]  Cheng Li,et al.  Unconstrained Face Alignment via Cascaded Compositional Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

[25]  Rama Chellappa,et al.  Face Alignment by Local Deep Descriptor Regression , 2016, ArXiv.

[26]  Motaz El-Saban,et al.  Human age estimation using enhanced bio-inspired features (EBIF) , 2010, 2010 IEEE International Conference on Image Processing.

[27]  Xin Liu,et al.  AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[28]  Huaizu Jiang,et al.  Face Detection with the Faster R-CNN , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[29]  Xiangyu Zhu,et al.  Face Alignment in Full Pose Range: A 3D Total Solution , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Tal Hassner,et al.  Do We Really Need to Collect Millions of Faces for Effective Face Recognition? , 2016, ECCV.

[32]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[33]  Luc Van Gool,et al.  Structured Output SVM Prediction of Apparent Age, Gender and Smile from Deep Features , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[34]  Sergio Escalera,et al.  ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016 , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[35]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Jian Sun,et al.  Face Alignment at 3000 FPS via Regressing Local Binary Features , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Jing Wang,et al.  Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Rama Chellappa,et al.  Unconstrained Age Estimation with Deep Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[40]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Rama Chellappa,et al.  HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Shengcai Liao,et al.  Learning Face Representation from Scratch , 2014, ArXiv.

[43]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[46]  Qiong Cao,et al.  Template Adaptation for Face Verification and Identification , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[47]  Yu Qiao,et al.  Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , 2016, IEEE Signal Processing Letters.

[48]  Shengcai Liao,et al.  A Fast and Accurate Unconstrained Face Detector , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Horst Bischof,et al.  Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[50]  Carlos D. Castillo,et al.  Triplet probabilistic embedding for face verification and clustering , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[51]  Luc Van Gool,et al.  Face Detection without Bells and Whistles , 2014, ECCV.

[52]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Koen E. A. van de Sande,et al.  Segmentation as selective search for object recognition , 2011, 2011 International Conference on Computer Vision.

[54]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[55]  Trevor Darrell,et al.  PANDA: Pose Aligned Networks for Deep Attribute Modeling , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[56]  Chi Fang,et al.  A new biologically inspired active appearance model for face age estimation by using local ordinal ranking , 2013, ICIMCS '13.

[57]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[58]  Dongqing Zhang,et al.  Neural Aggregation Network for Video Face Recognition , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[59]  Xiaoou Tang,et al.  Facial Landmark Detection by Deep Multi-task Learning , 2014, ECCV.

[60]  Rama Chellappa,et al.  A deep pyramid Deformable Part Model for face detection , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).