Palmprint Recognition Method Based on Multispectral Image Fusion
XU Xue-bin1, 2, XING Xiao-min1, 2*, AN Mei-juan1, 2, CAO Shu-xin1, 2, MENG Kan1, 2, LU Long-bin1, 2
1. The Department of Data Science and Big Data Technology, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
Abstract：Biometric identification plays an important role in the field of information security. As a new biometric identification method, Palmprint identification has the advantages of low distortion, non-invasiveness and high uniqueness. Traditional palmprint research mostly uses natural light imaging systems to acquire in grayscale format, and it is not easy to improve the recognition accuracy further. In order to obtain more identification information, a multispectral palmprint image is proposed to replace the natural light palmprint image. Aiming at the problem that the existing palmprint recognition algorithms do not consider the characteristics of different spectra, resulting in loss of texture details and low recognition accuracy, a palmprint recognition algorithm based on multi-spectral image fusion is proposed. This method decomposes the multispectral palmprint image into a series of two-dimensional intrinsic mode functions (BIMF) with frequencies from high to low and a Residual component, which can be regarded as a preliminary estimate of the low-frequency information of the spectral image. Since the illumination conditions are unstable during the image acquisition process, and the near-infrared spectral image is sensitive to illumination transformation during FABEMD decomposition, it is easy to cause the decomposed BIMF background information to be too redundant. Therefore, the background reconstruction of the decomposed near-infrared palmprint image is performed. And feature refinement, which effectively enhances the feature expression of high-frequency information while smoothing the background redundant information. In order to avoid the problem of image overexposure caused by the spectral information after direct fusion processing, it is proposed to compress the near-infrared features before fusion. In addition, an improved residual network (IRCANet) combined with an attention mechanism is proposed for palmprint image classification after fusion, and a staged residual structure is introduced into the network to alleviate the degradation problem of the network. For the fused multispectral palmprint image, the staged residual structure can stably transmit image information between networks, but the effect of distinguishing high and low-frequency information in the image is not significant enough. In order to make the network pay attention to More discriminative features, use the interdependence between feature channels and incorporate a channel attention mechanism in the staged residual structure. Finally, comprehensive experiments on the multispectral palmprint dataset of the Hong Kong Polytechnic University (PolyU) show that the method can achieve good results, and the algorithm recognition accuracy can reach 99.67% and has good real-time performance.
[1] Jain Anil K, Flynn Patrick Joseph, Ross Arun Abraham (Eds). Handbook of Biometrics. Springer, USA, 2008.
[2] Kong A, Zhang D, Kamel M. Pattern Recognition，2009, 42(7): 1408.
[3] Wang J G, Yau W Y, Suwandy A, et al. Pattern Recognition，2008, 41(5): 1514.
[4] Guo Z, Zhang D, Zhang L. IEEE Transactions on Information Forensics and Security，2012, 7(3): 1094.
[5] Xu Y, Fan Z, Qiu M, et al. Neurocomputing, 2013, 103: 164.
[6] Hao Y, Sun Z, Tan T. Proceedings of International Conference on Asian Conference on Computer Vision. 2007, Part 11: 12.
[7] MENG Xiang-chao, SHEN Huan-feng, ZHANG Hong-yan, et al(孟祥超, 沈焕锋, 张洪艳, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2014, 34(5): 1332.
[8] Xu X, Guo Z, Song C, et al. Sensors，2012, 12(4): 4633.
[9] Khan Z, Mian A, Hu Y. In Proceedings of 2011 International Conference on Computer Vision (ICCV 2011)，2011, 1935.
[10] Zhang D, Guo Z, Gong Y. IEEE Transactions on Instrumentation and Measurement，2010, 59(2): 480.
[11] WANG Yan-xia, RUAN Qiu-qi(王艳霞, 阮秋琦). Chinese Journal of Image and Graphics(中国图象图形学报)，2008, 8(6): 1115.
[12] Bhuiyan Sharif M A, Reza R Adhami, Jesmin F Khan. EURASIP Journal on Advances in Signal Processing，2008, 32(7): 1.
[13] Demir B, Ertürk S. IEEE Transactions on Geoscience and Remote Sensing，2010, 48(11): 4071.
[14] He K, Zhang X, Ren S, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016, 770. https://doi.org/10.1109/CVPR.2016.90.
[15] Szegedy C, Liu W, Jia Y, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015, 1. https://doi.org/10.1109/CVPR.2015.7298594.
[16] Huang G, Liu Z, Van Der Maaten L J P, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017, 4700. https://doi.org/10.1109/CVPR.2017.243.
[17] Simonyan K, Zisserman A. 2014 Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014, ArXirpreprint: 1409.
[18] Han D, Guo Z, Zhang D. Multispectral Palmprint Recognition Using Wavelet-Based Image Fusion, In Proceedings of the IEEE International Conference on Signal Processing (ICSP)，2008, 2074.
[19] Xu X, Guo Z, Song C, Li Y. Sensors，2012, 12(4): 4633.
[20] Minaee S, Wang Y. Subspace Learning in the Presence of Sparse Structured Oultiers and Noise. In 2017 IEEE International Symposium on Circuits and Systems (ISCAS)，2017, 1.
[21] Shao H, Zhong D. IEEE Transactions on Image Processing，2021, 30(2021): 3764.
[22] Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-V4, Inception-ResNet and the Impact of Residual Connections on Learning, Thirty-First AAAI Conference on Artificial Intelligence，2017, 1.
