The traditional face recognition methods were based on the feature extraction, but, the process of feature extraction needs the strong prior knowledge and plenty engineering experience. This paper introduces the deep tiled convolutional neural networks (DTCNN), which can learn the feature, to implement the face recognition, but the DTCNN will encounter many problems, such as costing too much time in training and occupying too much internal memory. This paper proposes a parallel deep tiled CNN using the framework of Map-Reduce to overcome these problems. The experimental results show that the performance of face recognition of our method is better than that of traditional method based on feature extraction, and system training time cost has been greatly decreased because of the parallel framework of Map-Reduce when testing the large scale dataset.
Key words
deep tiled convolutional neural networks /
Map-Reduce /
face recognition /
feature learning
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References
[1] 李文辉, 高璐, 林逸峰,等. 特征选择模糊加权多通道Gabor人脸识别[J]. 湖南大学学报:自然科学版, 2013, 40(4): 87-93.Li Wenhui, Gao Lu, Lin Yifeng, et al. Feature selection fuzzy weighted Multi-Gabor face recognition[J]. Journal of Hunan University: Natural Sciences, 2013, 40(4): 87-93.
[2] 戴金波, 肖霄, 赵宏伟. 基于低分辨率局部二值模式的人脸识别[J]. 吉林大学学报, 2013, 43(2): 435-438.Dai Jinbo, Xiao Xiao, Zhao Hongwei. Human face recognition based on low resolution local binary pattern[J]. Journal of Jinlin University, 2013, 43(2): 435-438.
[3] 郭礼华, 白洋, 金连文. PHOG特征与聚类特征选择的笑脸识别方法[J]. 模式识别与人工智能, 2012, 25(1): 23-28.Guo Lihua, Bai Yang, Jin Lianwen. Smile recognition based on PHOG feature extraction and clustering feature selection[J]. Pattern Recognition and Artificial Intelligence, 2012, 25(1): 23-28.
[4] Turk M, Pentland A. Face recognition using eigenfaces[C]//CVPR, 1991: 586-591.
[5] Belhumeur P, Hespanha J, Kriegman D. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
[6] Chen C F, Wei C P, Wang Y C F. Low-rank matrix recovery with structural incoherence for robust face recognition[C]//CVPR, 2012: 2618-2625.
[7] Le Q V, Ngiam J, Chen Z. Tiled convolutional neural network[C]//NIPS, 2010.
[8] Venkatraman S, Kulkarni S. MapReduce, neural network framework for efficient content based image retrieval from large datasets in the cloud[C]//2012 12th International Conference on Hybrid Intelligent Systems (HIS), 2012.
[9] http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. [2013-07-10].
[10] Huang G B, Mattar M, Berg T, et al. Labeled faces in the wild: A database for studying face recognition in unconstrained environments[R]. TechReport, 2007.
[11] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C]//CVPR, 2001: 511-518.
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