[1]ZHANG Yuanyuan,HUO Jing,YANG Wanqi,et al.A deep belief network-based heterogeneous face verification method for the second-generation identity card[J].CAAI Transactions on Intelligent Systems,2015,10(2):193-200.[doi:10.3969/j.issn.1673-4785.201405060]
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A deep belief network-based heterogeneous face verification method for the second-generation identity card

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 193-200 Column: 学术论文—机器感知与模式识别 Public date: 2015-04-25
Title:
A deep belief network-based heterogeneous face verification method for the second-generation identity card
Author(s):
ZHANG Yuanyuan;  HUO Jing;  YANG Wanqi;  GAO Yang;  SHI Yinghuan
State Key Laboratory for Software Technology, Nanjing University, Nanjing 210023, China
Keywords:
face recognition; multimodes; deep learning; deep belief network
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201405060
Abstract:
The objective of the face verification method for the second-generation identity card is to determine whether the original head-photo stored in the corresponding identity card image and the currently captured head photo of the card-holder by using a video camera image actually belongs to the same person or not. To obtain a good verification result for the heterogeneous face verification method is a very challenging task because the two different types of images belong to two different modalities (e.g., different image resolutions, different illumination conditions). Considering the difference of trans-modal face images, it is hard to use traditional feature extraction methods to extract discriminative feature for description of images with different modes. Traditional feature extraction methods cannot distinguish images exactly. In this paper, a deep learning-based face verification method is proposed. The proposed deep learning-based face verification method integrates the deep belief network (DBN), which employs unsupervised greedy layer-by-layer training for high-level feature extraction of face photo and combines the popularly used image preprocessing and similarity measurement technologies to realize the purpose of face verification. The results were evaluated on a real dataset with two different modalities of 256 different people. This method outperforms the traditional principal component analysis (PCA) and linear discriminant analysis (LDA) methods with 12% and 8% improvements in terms of the verification accuracy, respectively. The results validated the advantage of the proposed method, especially when the amount of entries increases.
References:
[1] CHAN C H, TAHIR M A, KITTLER J, et al. Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors[J]. Pattern Analysis and Machine Intelligence, 2013, 35(5): 1164-1177.
[2] LIAO S, JAIN A K, LI S Z, et al. Partial face recognition: alignment-free approach[J]. Pattern Analysis and Machine Intelligence, 2013, 35(5): 1193-1205.
[3] CAO X, WIPF D, WEN F, et al. A practical transfer learning algorithm for face verification[C]//The IEEE International Conference on Computer Vision. Sydney, Australia, 2013: 3208-3215.
[4] SIROVICH L, KIRBY M. Low-dimensional procedure for the characterization of human faces[J]. Journal of the Optical Society of America, 1987, 4(3): 519-524.
[5] TURK M A, PENTLAND A P. Face recognition using eigenfaces[C]//Computer Vision and Pattern Recognition.[S.l.], 1991: 586-591.
[6] SCHOLKOPFT B, MULLERT K R. Fisher discriminant analysis with kernels[C]//Proc of IEEE International Workshop on Neural Networks for Signal Processing. Madison, USA, 1999: 41-48.
[7] HE X, NIYOGI P. Locality preserving projections[C]//Annual Conference on Neural Information Processing Systems. British Columbia, Canada, 2003, 16: 234-241.
[8] BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks[C]//Neural Information Processing Systems. Vancouver, Canada, 2007, 19: 153-160.
[9] LIN D, TANG X. Inter-modality face recognition[J]. European Conference on Computer Vision, 2006, 3954(4): 13-26.
[10] HINTON G E, OSINDERO S, TEH Y W, et al. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
[11] HINTON G E. Learning multiple layers of representation[J]. Trends in Cognitive Sciences, 2007, 11(10): 428-434.
[12] BARKAN O, WEILL J, WOLF L, et al. Fast high dimensional vector multiplication face recognition[C]//The IEEE International Conference on Computer Vision. Sydney, Australia, 2013: 1-8.
[13] SIMONYAN K, PARKHI O M, VEDALDI A, et al. Fisher vector faces in the wild[C]//Proc British Machine Vision Conference. Bristol, UK, 2013: 1-12.
[14] CUI Z, LI W, XU D, et al. Fusing robust face region descriptors via multiple metric learning for face recognition in the wild[C]//Computer Vision and Pattern Recognition. Portland, Oregon, 2013: 3554-3561.
[15] KLARE B, JAIN A K. Heterogeneous face recognition: matching NIR to visible light images[C]//International Conference on Pattern Recognition. Istanbul, Turkey, 2010: 1513-1516.
[16] YI D, LIU R, CHU R, et al. Face matching between near infrared and visible light images[J]. Advances in Biometrics, 2007, 4642: 523-530.
[17] WANG R, YANG J, YI D, et al. An analysis-by-synthesis method for heterogeneous face biometrics[J]. Advances in Biometrics, 2009, 5558: 319-326.
[18] CHEN J, YI D, YANG J, et al. Learning mappings for face synthesis from near infrared to visual light images[C]//Computer Vision and Pattern Recognition. Fla, USA, 2009: 156-163.
[19] SUN Y, WANG X, TANG X, et al. Hybrid deep learning for face verification[C]//IEEE International Conference on Computer Vision. Sydney, Australia, 2013: 1489-1496.
[20] HUANG G B, RAMESH M, BERG T, et al. Labeled faces in the wild: a database for studying face recognition in unconstrained environments[R]. Massachusetts, Amherst, 2007: 07-49.
[21] HINTON G. A practical guide to training restricted Boltzmann machines[J]. Momentum, 2010, 9(1): 599-619.
[22] HINTON G.Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002,14(8): 1771-1800.
[23] HINTON G. Learning multiple layers of representation[J]. Trends in Cognitive Sciences, 2007, 11(10): 428-434.
[24] HINTON G. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[25] EDWARDS G J, COOTES T F, TAYLOR C J, et al. Face recognition using active appearance models[J]. Computer Vision—(ECCV). 1998,1407: 581-595.
[26] VIOLA P, JONES M. Fast and robust classification using asymmetric adaboost and a detector cascade[C]//Neural Information Processing Systems. Vancouver, Canada, 2002: 1311-1318.
[27] 余凯, 贾磊, 陈雨强, 等. 深度学习的昨天, 今天和明天[J]. 计算机研究与发展[J], 2013,50(9): 1799-1804.YU Kai, JIA Lei, CHEN Yuqiang, et al. Yesterday, today and tomorrow for deep learning[J]. Journal of Computer Research and Development, 2013, 50(9): 1799-1804.
[28] HUANG G B, LEE H, LEARNED-MILLER E, et al. Learning hierarchical representations for face verification with convolutional deep belief networks[C]//Computer Vision and Pattern Recognition. Newport, USA, 2012: 2518-2525.
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