Distribution alignment for cross-device palmprint recognition

Abstract

With the development of IoT and mobile devices, cross-device palmprint recognition is becoming an emerging research topic in multimedia for its great application potential. Due to the diverse characteristics of different devices, e.g.resolution or artifacts caused by post-processing, cross-device palmprint recognition remains a challenging problem. In this paper, we make efforts to improve cross-device palmprint recognition in two aspects: (1) we put forward a novel distribution-based loss to narrow the representation gap across devices, and (2) we establish a new cross-device benchmark based on existing palmprint recognition datasets. Different from many recent studies that only utilize instance-level or pairwise-level information between devices, the proposed progressive target distribution loss (PTD loss) uses the distributional information. Moreover, we establish a progressive target mechanism that will be dynamically updated during training, making the optimization easier and smoother. The newly established benchmark contains more samples and more types of IoT devices than previous benchmarks, which can facilitate cross-device palmprint research. Extensive comparisons on several benchmarks reveal that: (1) our method outperforms other cross-device biometric recognition approaches significantly; (2) our method presents superior performance compared to SOTA competitors on several general palmprint recognition benchmarks; Code and data are openly available at https://kaizhao.net/palmprint.