Enhancing thermal facial recognition leveraging large datasets and hybrid algorithms

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.

KEYWORDS:

• Thermal facial recognition
• hybrid algorithm
• multi-scale feature
• attention module
• joint loss function

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated during and/or analysed during the current study are available at ThermalFace/CSU_Laval_dataset-and-Algorithm(github.com).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [Grant No. 61505264].