Coarse-to-fine Palmprint-Specific Quality Feature Learning for Palmprint Image Quality Assessment

Palmprint recognition has aroused broad concern recently due to its several advantages, such as contactless, hygienic, and less-invasive properties. However, most existing palmprint recognition methods focus on feature extraction and matching without assessing the quality of palmprint images, making the recognition result sensitive []to low-quality images. To the best of our knowledge, there is still no literature with an attempt to specially study the problem of palmprint image quality assessment. To address this, in this paper, we propose an end-to-end palmprint-specific quality feature learning and assessment framework, which consists of an attention-embedded coarse feature learning network and a fine quality feature learning network. The coarse feature learning network aims to extensively explore the quality-related information from palmprint images by embedding texture maps into the palmprint images via an attention-embedded CNN network. Then, the fine quality feature learning network is learned to extract the latent quality-specific features of palmprint images. Moreover, we established a new quality-labeled palmprint image benchmark database based on an automatic quality labeling scheme. Experimental results on the new palmprint image benchmark database demonstrate that the proposed method consistently outperforms the state-of-the-art methods.