ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Feature fusion-based face verification on second generation identity card

  • 1.

    School of Computer Science and Technology, Hefei Normal University, Hefei 230601, China

  • 2.

    School of Computer Science and Technology, University of Science and Technology of China,Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.10.008
  • Received Date: 20 April 2018
  • Accepted Date: 17 December 2018
  • Rev Recd Date: 17 December 2018
  • Publish Date: 31 October 2019
    Abstract

  • Abstract

    The second-generation ID card face verification refers to judging whether the photo on the second-generation ID card matches its user Due to its low resolution, the second-generation ID card photo differs greatly from the photo taken on the spot in terms of clarity, facial changes, and the external environment, resulting in the low recognition rate of the conventional face recognition method. To solve this problem, t a second-generation ID card facial verification system based on feature fusion is proposed. The system consists of five parts: image acquisition, preprocessing, feature extraction, feature comparison, and result judgment. First, the second-generation ID card image and camera photo are collected and image preprocessing is performed. The global and local features of the second-generation card photo and camera photo are then extracted separately. Global features are extracted by PCA and LDA methods, and local features are extracted by the histogram directional binary code (HDBC) method. Then, the global and local features are calculated in the common feature space, and the similarity between the global features local features is obtained. Finally, the user of the second-generation ID card is tested based on the given threshold. Experiments have been performed on a large number of real second-generation ID card datasets. Compared with the traditional single feature extraction algorithms, the recognition rate of the proposed method is significantly improved.

    Abstract

    The second-generation ID card face verification refers to judging whether the photo on the second-generation ID card matches its user Due to its low resolution, the second-generation ID card photo differs greatly from the photo taken on the spot in terms of clarity, facial changes, and the external environment, resulting in the low recognition rate of the conventional face recognition method. To solve this problem, t a second-generation ID card facial verification system based on feature fusion is proposed. The system consists of five parts: image acquisition, preprocessing, feature extraction, feature comparison, and result judgment. First, the second-generation ID card image and camera photo are collected and image preprocessing is performed. The global and local features of the second-generation card photo and camera photo are then extracted separately. Global features are extracted by PCA and LDA methods, and local features are extracted by the histogram directional binary code (HDBC) method. Then, the global and local features are calculated in the common feature space, and the similarity between the global features local features is obtained. Finally, the user of the second-generation ID card is tested based on the given threshold. Experiments have been performed on a large number of real second-generation ID card datasets. Compared with the traditional single feature extraction algorithms, the recognition rate of the proposed method is significantly improved.
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    • References(25)
  • [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]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5): 1164-1177.
    [2]
    TURK M A, PENTLAND A P. Face recognition using eigenfaces[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . Maui, USA: IEEE, 1991: 586-591.
    [3]
    MIKA S, RATSCH G,WESTON J, et al. Fisher discriminant analysis with kernels[EB/OL]// [2019-03-26] https://www.researchgate.net/publication/3814316_Fisher_Discriminant_Analysis_with_Kernels. Neural Networks for Signal Processing IX, 1999.
    [4]
    BARTLETT M S, MOVELLAN J R, SEJNOWSKI TJ. Face recognition by independent component analysis[J]. IEEE Transactions on neural networks, 2002, 13(6): 1450-1464.
    [5]
    JAHANBIN S, CHOI H, BOVIK A C. Passive multimodal 2-D+ 3-D face recognition using Gabor features and landmark distances[J]. IEEE Transactions on Information Forensics and Security, 2011, 6(4): 1287-1304.
    [6]
    ZHU X, LEI Z, YANJ, et al. High-fidelity pose and expression normalization for face recognition in the wild[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 787-796.
    [7]
    ZHANG B, ZHANG L, ZHANG D, et al. Directional binary code with application to PolyU near-infrared face database[J]. Pattern Recognition Letters, 2010, 31(14): 2337-2344.
    [8]
    LOWE D G. Distinctive image features from scale-invariant key points[J]. International journal of computer vision, 2004, 60(2): 91-110.
    [9]
    LIU W, TANG X, LIU J. Bayesian tensor inference for sketch-based facial photo hallucination[C]// International Joint Conference on Artifical Intelligence. Hyderabad, India: Morgan Kaufmann Publishers, 2007: 2141-2146.
    [10]
    KLARE B F, BUCAK S S, JAINA K, et al. Towards automated caricature recognition[C]//2012 5th IAPR International Conference on Biometrics (ICB). New Delhi, India: IEEE, 2012: 139-146.
    [11]
    LIAO S, YI D, LEI Z, et al. Heterogeneous face recognition from local structures of normalized appearance[C]// International Conference on Biometrics. Berlin Heidelberg: Springer, 2009: 209-218.
    [12]
    KLARE B, JAIN AK. Sketch-to-photo matching: A feature-based approach[C]// SPIE Defense, Security, and Sensing. International Society for Optics and Photonics. Orlando, USA: IET, 2010: 766702-766702-10.
    [13]
    BHATT H S, BHARADWAJ S, SINGH R, et al. On matching sketches with digital face images[C]// Fourth IEEE International Conference on Biometrics: Theory Applications and Systems . Washington, USA: IEEE, 2010: 1-7.
    [14]
    KLARE B, JAIN A K. Heterogeneous face recognition: Matching nir to visible light images[C]// International Conference on Pattern Recognition . Istanbul, Turkey: IEEE, 2010: 1513-1516.
    [15]
    WANG R, YANG J, YI D, et al. An analysis-by-synthesis method for heterogeneous face biometrics[C]// International Conference on Biometrics. Berlin: Springer , 2009: 319-326.
    [16]
    HUANG D, ARDABILIAN M, WANG Y, et al. Automatic asymmetric 3D-2D face recognition[C]// International Conference on Pattern Recognition. Washington, USA: IEEE, 2010: 1225-1228.
    [17]
    ZHOU C, ZHANG Z, YI D, et al. Low-resolution face recognition via simultaneous discriminant analysis[C]// International Joint Conference on Biometrics. Washington, USA: IEEE, 2011: 1-6.
    [18]
    任小龙, 苏光大, 相燕. 使用第 2 代身份证的人脸识别身份认证系统[J]. 智能系统学报, 2009, 4(3): 213-217.
    [19]
    REN Xiaolong, SU Guangda, XIANG Yan. Face authentication system using the Chinese second generation identity card[J]. CAAI Transactions on Intelligent Systems, 2009, 4(3): 213-217.
    [20]
    倪鑫，雷震，邢辉，等.基于二代身份证信息的人脸识别系统[C]// 未来计算机与通信工程国际会议. 天津, 中国: 亚特兰蒂斯出版社, 2014: 184-188.
    [21]
    NI X, LEI Z, XING H, et al. Face recognition system based on the second generation identity card[C]// International Conference on Future Computer and Communication Engineering. Tianjin, China: Atlantis Press, 2014:184-188.
    [22]
    马聪. 终端信息感知的人脸图像处理技术研究与实现[D]. 东南大学, 2014.
    [23]
    张媛媛, 霍静, 杨婉琪,等. 深度信念网络的二代身份证异构人脸核实算法[J]. 智能系统学报, 2015, 10(2): 193-200.
    [24]
    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.
    [25]
    ZHU H, WANG Y, MAO X, et al. Block statistical features-based face verification on second generation identity card[C]//Chinese Conference on Biometric Recognition. Zhuzhou, China: Springer, 2015: 43-50.)
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    [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]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5): 1164-1177.
    [2]
    TURK M A, PENTLAND A P. Face recognition using eigenfaces[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . Maui, USA: IEEE, 1991: 586-591.
    [3]
    MIKA S, RATSCH G,WESTON J, et al. Fisher discriminant analysis with kernels[EB/OL]// [2019-03-26] https://www.researchgate.net/publication/3814316_Fisher_Discriminant_Analysis_with_Kernels. Neural Networks for Signal Processing IX, 1999.
    [4]
    BARTLETT M S, MOVELLAN J R, SEJNOWSKI TJ. Face recognition by independent component analysis[J]. IEEE Transactions on neural networks, 2002, 13(6): 1450-1464.
    [5]
    JAHANBIN S, CHOI H, BOVIK A C. Passive multimodal 2-D+ 3-D face recognition using Gabor features and landmark distances[J]. IEEE Transactions on Information Forensics and Security, 2011, 6(4): 1287-1304.
    [6]
    ZHU X, LEI Z, YANJ, et al. High-fidelity pose and expression normalization for face recognition in the wild[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 787-796.
    [7]
    ZHANG B, ZHANG L, ZHANG D, et al. Directional binary code with application to PolyU near-infrared face database[J]. Pattern Recognition Letters, 2010, 31(14): 2337-2344.
    [8]
    LOWE D G. Distinctive image features from scale-invariant key points[J]. International journal of computer vision, 2004, 60(2): 91-110.
    [9]
    LIU W, TANG X, LIU J. Bayesian tensor inference for sketch-based facial photo hallucination[C]// International Joint Conference on Artifical Intelligence. Hyderabad, India: Morgan Kaufmann Publishers, 2007: 2141-2146.
    [10]
    KLARE B F, BUCAK S S, JAINA K, et al. Towards automated caricature recognition[C]//2012 5th IAPR International Conference on Biometrics (ICB). New Delhi, India: IEEE, 2012: 139-146.
    [11]
    LIAO S, YI D, LEI Z, et al. Heterogeneous face recognition from local structures of normalized appearance[C]// International Conference on Biometrics. Berlin Heidelberg: Springer, 2009: 209-218.
    [12]
    KLARE B, JAIN AK. Sketch-to-photo matching: A feature-based approach[C]// SPIE Defense, Security, and Sensing. International Society for Optics and Photonics. Orlando, USA: IET, 2010: 766702-766702-10.
    [13]
    BHATT H S, BHARADWAJ S, SINGH R, et al. On matching sketches with digital face images[C]// Fourth IEEE International Conference on Biometrics: Theory Applications and Systems . Washington, USA: IEEE, 2010: 1-7.
    [14]
    KLARE B, JAIN A K. Heterogeneous face recognition: Matching nir to visible light images[C]// International Conference on Pattern Recognition . Istanbul, Turkey: IEEE, 2010: 1513-1516.
    [15]
    WANG R, YANG J, YI D, et al. An analysis-by-synthesis method for heterogeneous face biometrics[C]// International Conference on Biometrics. Berlin: Springer , 2009: 319-326.
    [16]
    HUANG D, ARDABILIAN M, WANG Y, et al. Automatic asymmetric 3D-2D face recognition[C]// International Conference on Pattern Recognition. Washington, USA: IEEE, 2010: 1225-1228.
    [17]
    ZHOU C, ZHANG Z, YI D, et al. Low-resolution face recognition via simultaneous discriminant analysis[C]// International Joint Conference on Biometrics. Washington, USA: IEEE, 2011: 1-6.
    [18]
    任小龙, 苏光大, 相燕. 使用第 2 代身份证的人脸识别身份认证系统[J]. 智能系统学报, 2009, 4(3): 213-217.
    [19]
    REN Xiaolong, SU Guangda, XIANG Yan. Face authentication system using the Chinese second generation identity card[J]. CAAI Transactions on Intelligent Systems, 2009, 4(3): 213-217.
    [20]
    倪鑫，雷震，邢辉，等.基于二代身份证信息的人脸识别系统[C]// 未来计算机与通信工程国际会议. 天津, 中国: 亚特兰蒂斯出版社, 2014: 184-188.
    [21]
    NI X, LEI Z, XING H, et al. Face recognition system based on the second generation identity card[C]// International Conference on Future Computer and Communication Engineering. Tianjin, China: Atlantis Press, 2014:184-188.
    [22]
    马聪. 终端信息感知的人脸图像处理技术研究与实现[D]. 东南大学, 2014.
    [23]
    张媛媛, 霍静, 杨婉琪,等. 深度信念网络的二代身份证异构人脸核实算法[J]. 智能系统学报, 2015, 10(2): 193-200.
    [24]
    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.
    [25]
    ZHU H, WANG Y, MAO X, et al. Block statistical features-based face verification on second generation identity card[C]//Chinese Conference on Biometric Recognition. Zhuzhou, China: Springer, 2015: 43-50.)
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