Da Ai
ABSTRACT
Face recognition technology is widely used in the field of public security. To improve the recognition accuracy under non-ideal lighting conditions, a face recognition method with dual-spectrum feature fusion is proposed using the property that the infrared spectrum is insensitive to visible light. The fused face images of visible and near-infrared spectra are obtained with the Non-Subsampled Shearlet Transform (NSST) algorithm, and then been put into the FaceNet as input and trained using transfer learning to renew the FaceNet model parameters for recognizing the fused face images. Compared with existing methods, experimental results show that the accuracy of face recognition is significantly improved under non-ideal lighting conditions which better meets the practical application requirements of public security.
- N. Alsrehin and M. A. Al-Taamneh, "Face Recognition Techniques using Statistical and Artificial Neural Network: A Comparative Study," 2020 3rd International Conference on Information and Computer Technologies (ICICT), San Jose, CA, USA, 2020, pp. 154-159 DOI: 10.1109/ICICT50521.2020.00032.Google Scholar
- J. G. Cavazos, P. J. Phillips, C. D. Castillo and A. J. O'Toole, "Accuracy Comparison Across Face Recognition Algorithms: Where Are We on Measuring Race Bias?," in IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 1, pp. 101-111, Jan. 2021, DOI: 10.1109/TBIOM.2020.3027269.Google ScholarCross Ref
- F. Wu, W. You, J. S. Smith, W. Lu, and B. Zhang, "Image-Image Translation to Enhance Near-Infrared Face Recognition," 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, 2019, pp. 3442-3446, DOI: 10.1109/ICIP.2019.8804414.Google Scholar
- Zhen Lei and S. Z. Li, "Coupled Spectral Regression for matching heterogeneous faces," 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, pp. 1123-1128, DOI: 10.1109/CVPR.2009.5206860.Google Scholar
- Y. Jin, J. Lu and Q. Ruan, "Coupled Discriminative Feature Learning for Heterogeneous Face Recognition," in IEEE Transactions on Information Forensics and Security, vol. 10, no. 3, pp. 640-652, March 2015, DOI: 10.1109/TIFS.2015.2390414.Google ScholarDigital Library
- R. He, X. Wu, Z. Sun and T. Tan, "Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 7, pp. 1761-1773, 1 July 2019, DOI: 10.1109/TPAMI.2018.2842770.Google ScholarCross Ref
- S. M. Iranmanesh, A. Dabouei, H. Kazemi and N. M. Nasrabadi, "Deep Cross Polarimetric Thermal-to-Visible Face Recognition," 2018 International Conference on Biometrics (ICB), Gold Coast, QLD, 2018, pp. 166-173, DOI: 10.1109/ICB2018.2018.00034.Google Scholar
- Y. Yang, M. Yang, S. Huang, Y. Que, M. Ding and J. Sun, "Multifocus Image Fusion Based on Extreme Learning Machine and Human Visual System," in IEEE Access, vol. 5, pp. 6989-7000, 2017, DOI: 10.1109/ACCESS.2017.2696119.Google ScholarCross Ref
- M. K. Bhowmik, D. Bhattacharjee, M. Nasipuri, D. K. Basu and M. Kundu, "Optimum fusion of visual and thermal face images for recognition," 2010 Sixth International Conference on Information Assurance and Security, 2010, pp. 311-316, DOI: 10.1109/ISIAS.2010.5604191.Google Scholar
- M. K. Bhowmik, B. K. De, D. Bhattacharjee, D. K. Basu, and M. Nasipuri, "Multisensor fusion of visual and thermal images for human face identification using different SVM kernels," 2012 IEEE Long Island Systems, Applications, and Technology Conference (LISAT), 2012, pp. 1-7, DOI: 10.1109/LISAT.2012.6223195.Google Scholar
- R. Raghavendra, S. Venkatesh, K. B. Raja, F. A. Cheikh and C. Busch, "Mutual Information Based Multispectral Image Fusion for Improved Face Recognition," 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2016, pp. 62-68, DOI: 10.1109/SITIS.2016.19.Google Scholar
- Z. M. Abood, G. S. Karam, and R. E. Haleot, "Face Recognition Using Fusion of Multispectral Imaging," 2017 Second Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), 2017, pp. 107-112, DOI: 10.1109/AIC-MITCSA.2017.8722957.Google Scholar
- G. Liu, S. Zhang and Z. Xie, "A novel infrared and visible face fusion recognition method based on non-subsampled contourlet transform," 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017, pp. 1-6, DOI: 10.1109/CISP-BMEI.2017.8301965.Google Scholar
- Q. Chen, J. Sun, V. Palade, X. Shi and L. Liu, "Hierarchical Clustering Based Band Selection Algorithm for Hyperspectral Face Recognition," in IEEE Access, vol. 7, pp. 24333-24342, 2019, DOI: 10.1109/ACCESS.2019.2897213.Google ScholarCross Ref
- S. Ghosh, R. Keshari, R. Singh and M. Vatsa, "Face identification from low-resolution near-infrared images," 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 938-942, DOI: 10.1109/ICIP.2016.7532495.Google Scholar
- M. Pavlović, B. Stojanović, R. Petrović, and S. Stanković, "Fusion of Visual and Thermal Imagery for Illumination Invariant Face Recognition System," 2018 14th Symposium on Neural Networks and Applications (NEURAL), 2018, pp. 1-5, DOI: 10.1109/NEUREL.2018.8586985.Google Scholar
- Zhang Baohua. Research on multi-source image fusion algorithm based on multi-scale transformation and sparse representation [D]. Shanghai University, 2016.Google Scholar
- N. S. Mishra and S. Dhabal, "On Combination of Fuzzy Memberships for Medical Image Fusion using NSST based Fuzzy-PCNN," 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), 2018, pp. 1-4, DOI: 10.1109/EAIT.2018.8470422.Google Scholar
- R. Zhang and L. Gao, "Medical Image Fusion Based on NonSubsampled Shearlet Transform and Parameter-Adaptive Pulse-Coupled Neural Network," 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE), 2021, pp. 77-81, DOI: 10.1109/ICECE54449.2021.9674366.Google Scholar
- X. Liu, G. Wang, and F. Zhao, "Multi-Focus Image Fusion Based on Non-Subsampled Shearlet Transform and Consistency Optimization," 2021 IEEE International Conference on Electronic Technology, Communication, and Information (ICETCI), 2021, pp. 587-590, DOI: 10.1109/ICETCI53161.2021.9563265.Google Scholar
- K. Guo and D. Labate, Optimally Sparse Multidimensional Representation using Shearlets, 2006.Google Scholar
- Imani M.A collaborative representation-based approximation method for remote sensing image fusion[J].Int J Remote Sens,2020,41(3):974-995.Google Scholar
- Yin M,Liu X,Liu Y,et al.Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain[J].IEEE Transactions on Instrumentation and Measurement,2019,68(1):49-64.Google Scholar
- A. L. da Cunha and M. N. Do, "On Two-Channel Filter Banks With Directional Vanishing Moments," in IEEE Transactions on Image Processing, vol. 16, no. 5, pp. 1207-1219, May 2007, DOI: 10.1109/TIP.2007.891798.Google ScholarCross Ref
- Y. Dong, Z. Lei, S. Liao, , "Learning face representation from scratch", Computer science, 2014.Google Scholar
- Stan Z. Li, Zhen Lei, Meng Ao, “The HFB Face Database for Heterogeneous Face Biometrics Research”. In the 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS, in conjunction with CVPR 2009). Miami, Florida, June 2009.Google ScholarCross Ref
- Dong Yi,Rufeng Chu,Zhen Lei,Stan Z.Li.Face Matching Between Near-Infrared and Visible Light Images. In Proceedings of IAPR/IEEE International Conference on Biometrics, (ICB-2007). Seoul, Korea, August 2007.Google Scholar
- B. F. Klare and A. K. Jain, "Heterogeneous face recognition using kernel prototype similarities", IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1410-1422, Jun. 2013.Google ScholarDigital Library
- D. Yi, Z. Lei, and S. Z. Li, "Shared representation learning for heterogenous face recognition," 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015, pp. 1-7, DOI: 10.1109/FG.2015.7163093.Google Scholar
- H. Roy and D. Bhattacharjee, "Heterogeneous face matching using zigzag pattern of local extremum logarithm difference: Zzpleld" in Advanced Computing and Systems for Security, Singapore: Springer, pp. 121-135, 2018.Google Scholar
- A. Kar, S. Pramanik, A. Chakraborty, D. Bhattacharjee, E. S. L. Ho, and H. P. H. Shum, "LMZMPM: Local Modified Zernike Moment Per-Unit Mass for Robust Human Face Recognition," in IEEE Transactions on Information Forensics and Security, vol. 16, pp. 495-509, 2021, DOI: 10.1109/TIFS.2020.3015552.Google ScholarDigital Library
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