Abstract
ABSTRACT Face recognition based on thermal image is a crucial aspect of identity verification that has been developed to counter low or no illumination. This paper proposes a novel hybrid algorithm for thermal face recognition to cope with the low resolution and texture blurring of thermal images. The algorithm contains a multi-scale feature fusion module, an attention module, and a joint loss function, which enhances the feature extraction capability, improves the classification accuracy, and has few network parameters. In addition to the innovative approach, a collaborative thermal facial dataset, named CSU-Laval, has been established by combining the 134 ULFMT dataset from Laval University, Canada, with 210 subjects acquired from Central South University, China. This dataset has 344 subjects and contains a rich set of face variables, including expression, angle, glasses-wearing, and time-lapse.
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