Abstract：To solve the problem of face recognition under unlimited conditions,a simple network structure named Inception Module Incorporated Siamese Convolutional Neural Networks (IMISCNN) was designed,which was suitable for small-scale data sets.On the basis of making full use of the Siamese structure to effectively reduce external interference and avoid over-fitting,inception module was incorporated to the Siamese network to extract richer features.Furthermore,a cyclical learning rate strategy was adopted to accelerate the convergence of the model.Simulation results on the CASIA-webface and Extended Yale B standard face database showed that the recognition accuracy of IMISCNN was significantly improved compared with other face recognition algorithms.
[1] Sundararajan K,Woodard D L.Deep learning for biometrics:A survey[J].ACM Computing Surveys (CSUR),2018,51(3):65.
[2] Nguyen K,Fookes C,Sridharan S,et al.Super-resolution for biometrics:A comprehensive survey[J].Pattern Recognition,2018,78:23-42.
[3] Takalkar M,Xu M,Wu Q,et al.A survey:facial micro-expression recognition[J].Multimedia Tools and Applications,2018,77(15):19301-19325.
[4] Sharma R,Patterh M S.A new hybrid approach using PCA for pose invariant face recognition[J].Wireless Personal Communications,2015,85(3):1561-1571.
[5] Tao Q Q,Zhan S,Li X H,et al.Robust face detection using local CNN and SVM based on kernel combination[J].Neurocomputing,2016,211:98-105.
[6] Lecun Y,Bengio Y,Hinton G.Deep learning[J].Nature,2015,521(7553):436-444.
[7] Chen G,Shao Y,Tang C,et al.Deep transformation learning for face recognition in the unconstrained scene[J].Machine Vision and Applications,2018,29(3):513-523.
[8] Atoum Y,Liu Y,Jourabloo A,et al.Face anti-spoofing using patch and depth-based cnns[A].2017 IEEE International Joint Conference on Biometrics (IJCB)[C].IEEE,2017.319-328.
[9] Bao J,Chen D,Wen F,et al.Towards open-set identity preserving face synthesis[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].IEEE,2018.6713-6722.
[10] 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. LI Qian-yu,JIANG Jian-guo,QI Mei-bin.Face recognition algorithm based on improved deep networks[J].Acta Electronica Sinica,2017,45(3):619-625.(in Chinese)
[11] Chan T H,et al.PCANet:A simple deep learning baseline for image classification?[J].IEEE Transactions on Image Processing,2015,24(12):5017-5032.
[12] 李小薪,梁荣华.有遮挡人脸识别综述:从子空间回归到深度学习[J].计算机学报,2018(1):177-207. LI Xiao-xin,LIANG Rong-hua.A review for face recognition with occlusion:from subspace regression to deep learning[J].Chinese Journal of Computers,2018(1):177-207.(in Chinese)
[13] Taigman Y,Yang M,Ranzato M A,et al.Deepface:Closing the gap to human-level performance in face verification[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].IEEE,2014.1701-1708.
[14] Schroff F,Kalenichenko D,Philbin J.Facenet:A unified embedding for face recognition and clustering[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].IEEE,2015.815-823.
[15] 吴长虹,苏剑波,陈叶飞.抗年龄干扰的人脸识别[J].电子学报,2018,46(7):1593-1600. WU Chang-hong,SU Jian-bo,CHEN Ye-fei.Age invariant face recognition[J].Acta Electronica Sinica,2018,46(7):1593-1600.(in Chinese)
[16] Szegedy C,Liu W,et al.Going deeper with convolutions[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].IEEE,2015.1-9.
[17] Smith L N.Cyclical learning rates for training neural networks[J].Computer Science,2015:464-472.
[18] Yi D,Lei Z,Liao S,et al.Learning face representation from scratch[J].arXiv preprint arXiv:1411.7923,2014.
[19] Lee K C,Ho J,Kriegman D J.Acquiring linear subspaces for face recognition under variable lighting[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2005,(5):684-698.
[20] Bromley J,Bentz J W,Bottou L,et al.Signature verification using a "SIAMESE" time delay neural network[A].International Conference on Neural Information Processing Systems[C].Morgan Kaufmann Publishers Inc,1993.737-744.
[21] 梁淑芬,刘银华,李立琛.基于LBP和深度学习的非限制条件下人脸识别算法[J].通信学报,2014,35(6):154-160. LIANG Shu-fen,LIU Yin-hua,LI Li-chen.Face recognition under unconstrained based on LBP and deep learning[J].Journal on Communications,2014,35(6):154-160.(in Chinese)
2020, Vol. 48 
