The neglected background cues can facilitate finger vein recognition. (April 2023)

• Record Type: Journal Article
• Authors: Zhao, Pengyang; Zhao, Shuping; Xue, Jing-Hao; Yang, Wenming; Liao, Qingmin
• Is Part Of: Pattern recognition. Volume 136(2023)
• Publication Date: 2023-04
• Subjects: Finger vein recognition -- Vein trait -- Background cue -- Intensity orientation vector -- Binary feature learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.109199
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We leverage the background variations in a finger vein image as a new auxiliary feature. We propose a new descriptor named Intensity Orientation Vector (IOV) for background cues. We propose a new supervised binary-feature learning scheme to jointly learn compact binary codes from both vein traits and background cues. Extensive experiments demonstrate the effectiveness of IOV and the binary-feature learning scheme. Abstract: Recently, finger vein based biometric authentication has attracted considerable attention due to its high efficiency and high security. However, most existing finger vein representation methods focus on vein traits while ignoring background cues, although background cues also convey identity information specific to each individual. In this paper, we leverage background intensity variations in finger vein images as new features to enrich discriminative representation, and accordingly propose a new descriptor named Intensity Orientation Vector (IOV). IOV, scaleable to reflect characteristics of finger tissues, offers additional informative cues for finger vein representation. Furthermore, we propose a new learning scheme named Semantic Similarity Preserved Discrete Binary Feature Learning (SSP-DBFL) for finger vein recognition. Unlike the most bimodal binary feature representation methods, SSP-DBFL preserves high-level semantic similarity in a common Hamming space to exploit the consensus between vein traits and background cues. Specifically, given a finger vein image, we first extract the direction difference vectors (DDV) as the main vein traits and the IOV as the auxiliary background cues. Subsequently, we jointly learn projection functions from these two types of features in a supervised manner, converting the two features into discriminative binary codes with their semantic similarity preserved. Finally, the binary codes are pooled into histogram-based vectors for finger vein representation. Extensive experiments are conducted on five widely used finger vein databases and demonstrate the effectiveness of our proposed IOV and SSP-DBFL.