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Face Recognition in Cross-spectral Environment using Deep Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 19 |
| Year of Publication: 2019 |
| Authors: Sana Khan, Zuber Farooqui |
10.5120/ijca2019919626
|
Sana Khan, Zuber Farooqui . Face Recognition in Cross-spectral Environment using Deep Learning. International Journal of Computer Applications. 177, 19 ( Nov 2019), 21-25. DOI=10.5120/ijca2019919626
Abstract
Face recognition is very much popular in the present era, and many researchers are working on face recognition and produce the most promising results in terms of recognition and human identification. It has many applications for authentication and verification. Along with these advancements, face recognition is still challenging the heterogamous environment, such as near-infrared and visible spectrum. Matching of face images capture in the near-infrared spectrum (NIR) to face images of the visible spectrum (VIS) is a very challenging task. In this research work, we have proposed a deep learning-based model for cross-spectral face matching (face recognition). The 26-layered deep residual network is extracted discriminative features from the face images and learn the common feature of the subject in the cross-spectral for the matching. To trained the proposed model, we have applied both VIS and NIR images with corresponding labels. For the performance evaluation of the proposed cross-spectral matching algorithm, experiments are performed on publicly available CASIA 2.0 NIR-VIS face datasets. The proposed method produced significant improvement in GAR. Our method gives recognition accuracy of 98.55 %.
References
- C. Sanderson, Biometric Person Recognition: Face, Speech and Fusion. VDM Publishing, 2008.
- S. Ouyang, T. Hospedales, Y. Song, and X. Li. A survey on heterogeneous face recognition: Sketch, infra-red, 3d, and low-resolution. In arXiv preprint arXiv:1409.5114, 2014.
- G. H. K. and S. T. Inter-modality face sketch recognition. In Multimedia and Expo, IEEE International Conference on, pages 224–229, 2012.
- B. Klare and A. K. Jain. Sketch-to-photo matching: a feature-based approach. In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, volume 7667, page 1, 2010.
- S. Z. Li, S. R. Chu, Liao, and L. Zhang. Illumination invariant face recognition using near-infrared images. PAMI, 29:627-639, 2007.
- S. Z. Li, D. Yi, Z. Lei, and S. Liao. The cassia nir-vis 2.0 face database. In Computer Vision and Pattern Recognition Workshops, IEEE International Conference on, pages 348– 353, 2013.
- X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Trans. Image Process., vol. 19, no. 6, pp. 1635–1650, Jun. 2010.
- B. Klare and A. Jain, “Heterogeneous face recognition: Matching NIR to visible light images,” in Proc. 20th ICPR, Aug. 2010, pp. 1513–1516.
- S. Liu, D. Yi, Z. Lei, and S. Li, “Heterogeneous face image matching using multi-scale features,” in Proc. 5th IAPRICB, Mar./Apr. 2012, pp. 79–84.
- D. Goswami, C. H. Chan, D. Windridge, and J. Kittler, “Evaluationof face recognition system in heterogeneous environments (visible vsNIR),” in Proc. IEEE ICCVW, Nov. 2011, pp. 2160–2167.
- S. Liao, D. Yi, Z. Lei, R. Qin, and S. Li, “Heterogeneous face recognitionfrom local structures of normalized appearance,” in Proc. 3rd Int. Conf.ICB, Jun. 2009, pp. 209–218.
- D. Yi, S. Liao, Z. Lei, J. Sang, and S. Li, “Partial face matching betweennear infrared and visual images in MBGC portal challenge,” in Proc.3rd Int. Conf. ICB, Jun. 2009, pp. 733–742.
- T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolutiongray-scaleand rotation invariant texture classification with local binary patterns,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987,Jul. 2002.
- Xiaoxiang Liu, Lingxiao Song, Xiang Wu, and TieniuTan, “Transferring Deep Representation for NIR-VIS Heterogeneous Face Recognition,” Int. Conf. on Biometrics (ICB), pp. 1-8, 2016
- Hailin Shi, Xiaobo Wang, Dong Yi, Zhen Lei, Xiangyu Zhu, and Stan Z. Li, Cross-Modality Face Recognition viaHeterogeneous Joint Bayesian, IEEE SIGNAL PROCESSING LETTERS, VOL. 24, NO. 1, JANUARY 2017
- Hu, Weipeng, and Haifeng Hu. "Disentangled Spectrum Variations Networks for NIR-VIS Face Recognition." IEEE Transactions on Multimedia (2019).
- Peng, Chunlei, et al. "Re-ranking High-Dimensional Deep Local Representation for NIR-VIS Face Recognition." IEEE Transactions on Image Processing (2019).
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