基于LBP和深度学习的非限制条件下人脸识别算法

doi:10.3969/j.issn.1000-436x.2014.06.020

Abstract

Abstract:

A face recognition method under unconstrained condition was proposed based on deep learning. At the same time, making LBP texture features as the input of deep learning net, and greedy training the network layer was made by layer to obtain good network parameters. At last, the trained net was used to predict the test samples' labels. The results of experiments on LFW(labeled faces in the wild) show that the algorithm can obtain higher recognition rate than traditional algorithms(such as PCA, SVM, LBP).Otherwise, the recognition rate on Yale and Yale-B are also very high, the experi-mental results show that deep learning net with LBP texture as its input can classify face images correctly.

Key words: unconstrained condition, face recognition, LBP, deep network, deep learning

Shu-fen LIANG,Yin-hua LIU,Li-chen LI. Face recognition under unconstrained based on LBP and deep learning[J]. Journal on Communications, 2014, 35(6): 154-160.

Figures/Tables 9

References 17

[1]	MEDIONI G , CHOI J , KUO C H , et al. Identifying noncooperative subjects at a distance using face images and inferred three dimensional face models[J]. IEEE Trans Syst, Man, Cybern A, Syst, Humans, 2009,39(1): 12-24.
[2]	BLANZ V , VETTER T . Face recognition based on fitting a 3D morphable model[J]. IEEE Transaction on Pattern Analysis and Ma-chine Intelligence, 2003,25(9): 1063-1074.
[3]	LIOR W , TAL H , YANIV T . Effective uncon-strained face recognition by combining multiple descriptors and learned background statistics[J]. IEEE Pattern Analysis and Machine Intelligence, 2011,33(10): 1978-1990.
[4]	MARSICO M D E , NAPPI M , RICCO D . Robust face recognition for uncontrolled pose and illumination changes[J]. IEEE Transactions on Systems, Man and Cybernetic, 2012,43(1): 149-163.
[5]	JAVIER R , RODRIGO V , MAURICIO C . Recognition of faces in unconstrained environments: a comparative study[J]. Journal on Ad-vances in Signal Processing. 2009,12(4): 44-69.
[6]	WOLF L , HASSNER T , TAIGMAN Y . Descriptor based methods in the wild[A]. Faces in Real-life Images Workshop in ECCV[C]. 2008.1-14.
[7]	ZHAO D , LIN Z , XIAO R , et al. Linear laplacian discrimination for feature extraction[A]. Proc IEEE Conference on Computer Vision and Pattern Recognition[C]. 2009.1-7.
[8]	BENGIO Y , DELALLEAU O . On the expressive power of deep archi-tectures[A]. Proc of 14th International Conference on Discovery Sci-ence[C]. Berlin: Springer-Verlag, 2011.18-36.
[9]	HINTON G E , OSINDERO S , THE Y-W . A fast learning algorithm for deep belief nets[A]. Neural Computation, 2006,18(7): 1527-1554.
[10]	COTTRELL G W . New life for neural networks[J]. Science, 2006,313(5786): 454-455.
[11]	TALOR G W , HINTON G E , ROWEIS S T . Modeling human motion using binary latent variables[A]. Advances in Neural Information Processing Systems[C]. 2007.1345-1352.
[12]	ITAMAR A , DEREK C R , THOMAS P K . Deep machine learning—a new frontier[J]. Artificial Intelligence Research IEEE Computa-tional Intelligence Magazine, 2010,5(4): 13-18.
[13]	OJALA T , PIETIKAINEN M , MAENPAA T . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intel-ligence, 2002,24(7): 971-987.
[14]	ZHAO Z , PIETIKAINEN M . Dynamic texture recognition using local binary patterns with an application to facial expressions[J]. IEEE Trans PAMI, 2007,27(6): 915-928.
[15]	LEI Z , LIAO S . Face recognition by exploring information jointly in space, scale and orientation[J]. IEEE Trans on Image Processing, 2009,2(1): 1-127.
[16]	LEI Z , LIAO S . Face recognition by exploring information jointly in space, scale and orientation[J]. IEEE Trans on Image Processing, 2009,2(1): 1-127.
[17]	HINTON G E . Face recognition by exploring information jointly in space, scale and orientation[J]. Neural Computation, 2002,14(8): 1771-1800.

Metrics

Recommended 0

No Suggested Reading articles found!

方法	隐藏单元数	识别率	训练时间/s	分类时/s	总时间/s
	1 000	24.83%	149.17	6.36	155.53
	2 000	45.15%	383.52	15.81	399.33
DBN	3 000	61.45%	720.31	30.34	750.65
	4 000	74.57%	1 164.53	45.39	1 209.92
	5 000	81.30%	1 706.08	67.48	1 773.56
	1 000	24.24%	471.84	13.05	484.89
传统LBP+DBN	2 000	45.56%	859.80	22.86	882.66
	3 000	61.65%	2 745.55	78.59	2 824.14
	1 000	25.88%	156.58	6.72	163.3
	2 000	47.92%	403.27	17.30	420.57
LBP+DBN	3 000	62.63%	766.61	28.45	795.06
	4 000	75.62%	1 173.92	47.30	1 221.22
	5 000	82.77%	1 743.00	68.06	1 811.06

方法		训练样本数
方法
	5	10	15
PCA	31.92%	38.98%	39.46%
SVM	36.58%	48.29%	52.40%
LBP	38.64%	51.43%	56.27%
DBN	81.30%	82.59%	82.99%
LBP+DBN	82.77%	84.02%	84.54%

方法	识别率
PCA	35.93%
SVM	45.51%
LBP	47.30%
FACE	61.00%
DBN	84.05%
LBP+DBN	85.16%

方法	1 000	2 000	3 000	4 000	5 000
DBN	44.21%	67.78%	80.48%	90.00%	96.67%
LBP+DBN	46.84%	68.29%	81.89%	90.14%	98.89%

方法	识别率
PCA	80.00%
SVM	94.44%
LBP	93.00%
DBN	96.67%
LBP+DBN	98.89%

Face recognition under unconstrained based on LBP and deep learning

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 17

Related Articles 15

Metrics

Recommended 0

方法	子集	1 000	2 000	3 000	4 000	5 000
	Sub2	52.12%	73.07%	84.93%	91.52%	96.67%
DBN
	Sub3	48.50%	71.13%	84.34%	89.57%	95.67%
	Sub2	54.70%	74.84%	85.16%	92.11%	97.08%
LBP+DBN
	Sub3	51.74%	73.72%	84.44%	90.87%	96.17%

[1]	Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG. Research on test strategy for randomness based on deep learning [J]. Journal on Communications, 2023, 44(6): 23-33.
[2]	Rongpeng LI, Bingyan WANG, Honggang ZHANG, Zhifeng ZHAO. Design of knowledge enhanced semantic communication receiver [J]. Journal on Communications, 2023, 44(6): 70-76.
[3]	Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI. Research on geomagnetic indoor high-precision positioning algorithm based on generative model [J]. Journal on Communications, 2023, 44(6): 211-222.
[4]	Jie YANG, Biao DONG, Xue FU, Yu WANG, Guan GUI. Lightweight decentralized learning-based automatic modulation classification method [J]. Journal on Communications, 2022, 43(7): 134-142.
[5]	Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI. Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF [J]. Journal on Communications, 2022, 43(6): 58-70.
[6]	Yong LIAO, Shiyi WANG. CSI feedback algorithm based on RM-Net for massive MIMO systems in high-speed mobile environment [J]. Journal on Communications, 2022, 43(5): 166-176.
[7]	Yurong LIAO, Haining WANG, Cunbao LIN, Yang LI, Yuqiang FANG, Shuyan NI. Research progress of deep learning-based object detection of optical remote sensing image [J]. Journal on Communications, 2022, 43(5): 190-203.
[8]	Zenghua ZHAO, Yuefan TONG, Jiayang CUI. Device-independent Wi-Fi fingerprinting indoor localization model based on domain adaptation [J]. Journal on Communications, 2022, 43(4): 143-153.
[9]	Yong LIAO, Gang CHENG, Yujie LI. CSI feedback algorithm based on deep unfolding for massive MIMO systems [J]. Journal on Communications, 2022, 43(12): 77-88.
[10]	Xueyuan DUAN, Yu FU, Kun WANG, Bin LI. LDoS attack detection method based on simple statistical features [J]. Journal on Communications, 2022, 43(11): 53-64.
[11]	Junyan HUO, Ruipeng QIU, Yanzhuo MA, Fuzheng YANG. Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture [J]. Journal on Communications, 2022, 43(11): 136-147.
[12]	Haiyan KANG, Yuanrui JI. Research on federated learning approach based on local differential privacy [J]. Journal on Communications, 2022, 43(10): 94-105.
[13]	Hongxia ZHANG, Qi WANG, Dengyue WANG, Ben WANG. Honeypot contract detection of blockchain based on deep learning [J]. Journal on Communications, 2022, 43(1): 194-202.
[14]	Yan YAN, Yiming CONG, Mahmood Adnan, Quanzheng SHENG. Statistics release and privacy protection method of location big data based on deep learning [J]. Journal on Communications, 2022, 43(1): 203-216.
[15]	Ye ZHU, Yilin YU, Yingchun GUO. HRDA-Net: image multiple manipulation detection and location algorithm in real scene [J]. Journal on Communications, 2022, 43(1): 217-226.