非限定条件下无约束的多姿态人脸关键特征自动识别算法

doi:10.11896/j.issn.1002-137X.2019.09.037

Abstract

Abstract: Automatic recognition of multi-pose faces key features is of great significance to the processing of images in face database.In order to ensure that face key features are accurately recognized,it is necessary to extract key features of the face.When the traditional algorithm is used to automatically recognize multi-pose face key features,the obtained face images are of poor efficiency,low recognition rate and low efficiency.This paper presented an automatic multi-pose face feature recognition algorithm based on vector machine.The 3D coordinate of the face key feature image is represented by the focal length of the camera,and the 3D information of the multi-pose face key feature is calculated.Filter is used to deal with multi-pose face key features.Finally,according to the weight of the vector machine,this paper analyzed the target function and the noise of face key features,calculated the condition probability and the iteration number of the face automatic recognition,and realized the automatic recognition of the key features of unconstrained multi-pose face under the unqualified condition.Experiment results show that the proposed algorithm can be used to automatically identifiy the multi-pose face key features,and has high recognition rate and recognition efficiency.

Key words: Automatic identification, Face key feature, Unconstrained multi-pose, Unqualified conditions

CLC Number:

TP391.41

ZHAO Zhi-wei, NI Gui-qiang. Automatic Recognition Algorithm for Unconstrained Multi-pose Face Key Features under Unqualified Conditions[J].Computer Science, 2019, 46(9): 250-253.

References

[1]LONG H Q,TAN T Z.Based on the Depth of Theconvolution Network and Local Binary Pattern of Face Recognition[J].Computer Simulation,2017,34(1):322-325.(in Chinese)龙海强,谭台哲.基于深度卷积网络算法的人脸识别方法研究[J].计算机仿真,2017,34(1):322-325.
[2]LI Q Y,JIANG J G,QI M B.Face Recognition Algorithm Based on Improved Deep Networks[J].Acta Electronica Sinica,2017,45(3):619-625.(in Chinese)李倩玉,蒋建国,齐美彬.基于改进深层网络的人脸识别算法[J].电子学报,2017,45(3):619-625.
[3]ZHANG J M,LIU Y C,WU H L,et al.A face recognition algorithm based on Gabor feature and projective dictionary pair learning[J].Computer Engineering and Science,2016,38(3):542-548.(in Chinese)张建明,刘阳春,吴宏林,等.基于Gabor特征与投影字典对学习的人脸识别算法[J].计算机工程与科学,2016,38(3):542-548.
[4]SHOU Z Y,YANG X F,LI M Y.Face recognition based on local feature and kernel low-rank representation[J].Application of Electronic Technique,2016,42(9):126-128.(in Chinese)首照宇,杨晓帆,李萌芽.基于局部特征与核低秩表示的人脸识别算法[J].电子技术应用,2016,42(9):126-128.
[5]WANG R.Based on the research of Adaboost face recognition algorithm[J].Electronic Design Engineering,2017,25(16):190-193.(in Chinese)王冉.基于Android平台人脸识别算法的应用[J].电子设计工程,2017,25(16):190-193.
[6]LI M,CAO L.The Face Sketch Synthesis Based on Sub-blocks LBP and Optimization[J].Bulletin of Science and Technology,2017,33(8):170-174.(in Chinese)李猛,曹林.基于子块LBP和最优相关的素描人脸合成[J].科技通报,2017,33(8):170-174.
[7]XIE W D.Research on Intelligent Recognition and Improvement of Facial Expression in Cloud Computing Environment[J].Computer Measurement & Control,2017,25(5):162-164.(in Chinese)谢文达.云计算环境下人脸表情智能识别改进技术研究[J].计算机测量与控制,2017,25(5):162-164.
[8]FENG Y P,AN X M.Subspace Face Recognition AlgorithmFused with Speed-up Robust Features[J].Science Technology and Engineering,2017,17(6):220-225.(in Chinese)冯宇平,安雪美.融合加速稳健特征的子空间人脸识别方法[J].科学技术与工程,2017,17(6):220-225.
[9]WANG X H,ZHAO Z X.PCA face recognition algorithm combined with gamma transform and wavelet transform[J].Computer Engineering and Applications,2016,52(5):190-193.(in Chinese)王晓华,赵志雄.结合伽马变换和小波变换的PCA人脸识别算法[J].计算机工程与应用,2016,52(5):190-193.
[10]ZHAO Z G,JU Z,GU H.Research on Low Resolution Face Recognition with Pose Variations[J].Control Engineering of China,2016,23(7):1057-1062.(in Chinese)赵志国,鞠哲,顾宏.低分辨率多姿态人脸识别算法研究[J].控制工程,2016,23(7):1057-1062.
[11]ABDALMAGEED W,WU Y,NEVATIA R,et al.Face recognition using deep multi-pose representations[C]//2016 IEEE Winter Conference on Applications of Computer Vision.IEEE,2016:1-9.
[12]SEO J J,KIM H I,YONG M R.Pose-Robust and Discriminative Feature Representation by Multi-task Deep Learning for Multi-view Face Recognition[C]//IEEE International Symposium on Multimedia.IEEE,2016:166-171.
[13]YANG J.Multi-pose Face Recognition Algorithm Based onSparse Representation[C]//International Conference on Intelligent Transportation,Big Data & Smart City.IEEE Computer Society,2016:113-116.
[14]YANG Z B,HOU L Y,YANG D L.Improved face recognition algorithm of attitude correction[J].Microcomputer & its Applications,2016,35(3):56-60.(in Chinese)杨作宝,侯凌燕,杨大利.改进的多姿态矫正的人脸识别算法[J].微型机与应用,2016,35(3):56-60.
[15]WU K,ZHU H L,HAO Y Y,et al.Cascade regression based multi-pose face alignment[J].Journal of Image and Graphics,2017,22(2):257-264.(in Chinese)伍凯,朱恒亮,郝阳阳,等.级联回归的多姿态人脸配准[J].中国图象图形学报,2017,22(2):257-264.
[16]LIAO H,LU S,WANG D.Tied factor analysis for uncon-strained face pose classification[J].Optik-International Journal for Light and Electron Optics,2016,127(23):11553-11566.
[17]JONATHON PHILLIPS P,MOON H,RIZVI S A,et al.The Feret evaluation methodology for face recognition algorithms[J].IEEE Transactions onPattern Analysis and Machine Intelligence,2000,22(10):1090-1104.MA H,SUN W C,SHI J H,et al.A Low-Resolution of Face Recognition Method Based on Curvelet Transform [J].Journal of Chongqing University of Technology(Natural Science),2018,32(11):168-174.(in Chinese)马慧,孙万春,史君华,等.基于Curvelet变换的低分辨率人脸识别方法[J].重庆理工大学学报(自然科学),2018,32(11):168-174.ZHU Z G, HE M X.Gender discriminant model of face based on local feature and depth neural network[J]. Machine Tool & Hydraulics,2018,46(6):127-132,151.(in Chinese)朱正国,何明星.基于局部特征和深度神经网络的人脸性别判别模型研究[J].机床与液压,2018,46(6):127-132,151.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Automatic Recognition Algorithm for Unconstrained Multi-pose Face Key Features under Unqualified Conditions

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 7

Metrics

Comments

Recommended 0

[1]	YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[2]	YOU Lan, HAN Xue-wei, HE Zheng-wei, XIAO Si-yu, HE Du, PAN Xiao-meng. Improved Sequence-to-Sequence Model for Short-term Vessel Trajectory Prediction Using AIS Data Streams [J]. Computer Science, 2020, 47(9): 169-174.
[3]	WANG Peng, SU Wei, ZHANG Jiu-wen, LIU Ying-Jie and WANG Zhen-rui. Prediction of Vessel Load Based on Vessel Automatic Identification System and Artificial Neural Network [J]. Computer Science, 2020, 47(6A): 49-53.
[4]	XU Kai, QIU Jia-yu and LI Yan. Offline Efficient Compression Algorithm for AIS Data Retains Time Elapsing Dimension [J]. Computer Science, 2017, 44(Z11): 498-502.
[5]	LAN Yi-yong, LIU Hai-feng and YANG Yuan-yuan. Minority Language Websites’ Automatic Identification and Collection [J]. Computer Science, 2015, 42(Z6): 79-82.
[6]	YANG Jin-cai GUO Kai-kai SHEN Xian-jun HU Jin-zhu. Automatic Identification and Rule Mining for Relation Words of Chinese Compound Sentences Based on Bayesian Model [J]. Computer Science, 2015, 42(7): 291-294.
[7]	YANG Jin-cai,XIE Fang and HU Jin-zhu. Research on Rule Engine for Automatic Identification of Relational Words in Chinese Complex Sentences [J]. Computer Science, 2014, 41(Z11): 25-28.