Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 246-250.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Face Attributes in Wild Based on Deep Learning

GE Hong-kong, LUO Heng-li, DONG Jia-yuan   

  1. (School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: Faces in the wild are huge in number and more close to life,and the recognition of facial attributes is a valuable research.A face attributes recognition method named RMLARNet (Regional Multiple Layer Attributes Related Net) was proposed for faces in the wild,which explores a new feature extraction method and attributes relationship.The processing steps of this method are as follows:1)Feature extraction is based on the regional parts of image.2)Features are extracted from different layer of Inception V3,and they are concatenated to get the final face feature.3)An attributes relationship related network is used for attributes recognition.The experiment is conducted on a balanced CelebA- data set which is a subset of CelebA,and this method outperforms state-of-the art methods.

Key words: Attributes constraint, Deep learning, Face attributes recognition, Multi-label task, Transfer learning

CLC Number: 

  • TP391.4
[1]KUMAR N,BERG A C,BELHUMEUR P N,et al.Attribute and simile classifiers for face verification[C]∥IEEE International Conference on Computer Vision.IEEE,2009:365-372.
[2]SONG F,TAN X,CHEN S.Exploiting relationship between attributes for improved face verification [J].Computer Vision & Image Understanding,2014,122(4):143-154.
[3]BOURDEV L,MAJI S,MALIK J.Describing people:A poselet-based approach to attribute classification[C]∥IEEE International Conference on Computer Vision.IEEE,2011:1543-1550.
[4]ZHANG N,PALURI M,RANZATO M,et al.PANDA:Pose Aligned Networks for Deep Attribute Modeling[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2014:1637-1644.
[5]LiU Z,LUO P,WANG X,et al.Deep Learning Face Attributes in the Wild[C]∥IEEE International Conference on Computer Vision.IEEE,2015:3730-3738.
[6]DONAHUE J,JIA Y,VINYALS O,et al.DeCAF:a deep convolutional activation feature for generic visual recognition[C]∥International Conference on International Conference on Machine Learning.JMLR.org,2014:I-647.
[7]SRINIVAS N,ATWAL H,ROSE D C,et al.Age,Gender,and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset[C]∥IEEE International Conference on Automatic Face & Gesture Recognition.IEEE,2017:953-960.
[8]ZENG T,JI S.Deep Convolutional Neural Networks for Multi-instance Multi-task Learning[C]∥IEEE International Conference on Data Mining.IEEE,2016:579-588.
[9]DONG M,PANG K,WU Y,et al.Transferring CNNS to multi-instance multi-label classification on small datasets[C]∥IEEE International Conference on Image Processing.Beijing:IEEE,2017:1332-1336.
[10]GHOSH S,LAKSANA E,SCHERER S,et al.A multi-labelconvolutional neural network approach to cross-domain action unit detection[C]∥International Conference on Affective Computing and Intelligent Interaction.IEEE,2015:609-615.
[11]SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the Inception Architecture for Computer Vision[C]∥Computer Vision and Pattern Recognition.IEEE,2016:2818-2826.
[12]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[13]ZOU W Y,ZHU S,NG A Y,et al.Deep learning of invariant features via simulated fixations in video[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc,2012:3203-3211.
[14]COATES A,NG A Y.Selecting receptive fields in deep net-works[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc,2011:2528-2536.
[15]ZHANG M L,ZHANG K.Multi-label learning by exploiting label dependency[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2010:999-1008.
[16]HUANG S J,ZHOU Z H.Multi-label learning by exploiting label correlations locally[C]∥Twenty-Sixth AAAI Conference on Artificial Intelligence.AAAI Press,2012:949-955.
[17]KUMAR N,BELHUMEUR P,NAYAR S.FaceTracer:ASearch Engine for Large Collections of Images with Faces[C]∥Europican Conference on Computer Vision.Springer-Verlag,2008:340-353.
[18]LI J,ZHANG Y.Learning SURF Cascade for Fast and Accurate Object Detection[C]∥Computer Vision and Pattern Recognition.IEEE,2013:3468-3475.
[19]SUN Y,WANG X,TANG X.Deep Convolutional Network Cascade for Facial Point Detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2013:3476-3483.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[3] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[4] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[5] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[6] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[7] FANG Yi-qiu, ZHANG Zhen-kun, GE Jun-wei. Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning [J]. Computer Science, 2022, 49(8): 70-77.
[8] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[9] HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163.
[10] ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169.
[11] SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235.
[12] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[13] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[14] ZHU Wen-tao, LAN Xian-chao, LUO Huan-lin, YUE Bing, WANG Yang. Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN [J]. Computer Science, 2022, 49(6A): 378-383.
[15] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!