Cross-spectral matching of active infrared (IR) facial probes to a visible light facial gallery is a new challenging problem. This scenario is brought up by a number of real-world surveillance tasks such as recognition of subjects at night or under severe atmospheric conditions. When combined with long distance, this problem becomes even more challenging due to deteriorated quality of the IR data, causing another issue called image quality disparity between the visible light and the IR imagery. To address this quality disparity in the heterogeneous images due to atmospheric and camera effects - typical degrading factors observed in long range IR data, we propose an image fusion-based method which fuses multiple IR facial images together and yields a higher-quality IR facial image. Wavelet decomposition using the Harr basis is conducted first and then the coefficients are merged according to a rule that treats the high and low frequencies differently, followed by an inverse wavelet transform step to reconstruct the final higher-quality IR facial image. Two sub-bands of the IR spectrum, namely short-wave infrared (SWIR) and near-infrared (NIR), as well as two different long standoffs of 50 m and 106 m are involved. Experiments show that in all cases of different sub-bands and standoffs our image fusion-based method outperforms the one without image fusion, with GARs significantly increased by 3.51% and 1.09% for SWIR 50 m and NIR 50 m at FAR=10%, respectively. The equal error rates are reduced by 2.61% and 0.90% for SWIR 50 m and NIR 50 m, respectively.
Zhicheng Cao, Yuanming Zhao, Heng Zhao, Xuan Xu, and Liaojun Pang, "Cross-spectral face recognition with image quality disparity using image fusion," Proc. SPIE 10988, Automatic Target Recognition XXIX, 1098811 (Presented at SPIE Defense + Commercial Sensing: April 18, 2019; Published: 14 May 2019); https://doi.org/10.1117/12.2515606.
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