BEST: Building evidences from scattered templates for accurate contactless palmprint recognition

•New framework to accurately and most efficiently match real-world contactless and cross-sensor palmprint images.•Outperforming results over state-of-the-art methods, using most challenging match protocols, on several publicly accessible datasets.•Significantly faster than previous approaches introduced in the contactless palmprint literature while achieving much higher match accuracy.•Rigorous cross-database and cross-sensor performance evaluation, using palm images from mobile phones, and ROC from over 35 million match scores. Contactless palmprint identification offers significantly improved hygiene and user convenience, making it highly attractive for a range of civilian applications, especially during the current pandemic. However, the accurate recognition of contactless palmprint images can be highly challenging, attributed to the significant variations in the intra-class similarity and limitations of conventional palmprint feature descriptors under involuntary or contactless imaging variations. State-of-the-art completely contactless palmprint matching algorithms in the literature cannot adequately address these challenges and are not sufficiently accurate and fast enough for such real-world applications. This paper proposes a novel approach that adaptively locates the local palmprint regions with high similarities between their corresponding feature representations or templates to address these challenges. We consider spatial localization of such highly similar feature representations from multiple local regions and consolidate them to generate a more reliable match score. This paper presents reproducible and comparative experimental results, using within-database, cross-database, and cross-sensor performance evaluation, on four publicly available contactless palmprint datasets, including a sizeable contactless palmprint database from 600 different subjects. The proposed method achieves outperforming results compared with three state-of-the-art deep learning-based methods and five widely used conventional methods. In addition, the proposed method is also significantly faster than all state-of-the-art baseline methods.