Suspecting Less and Doing Better: New Insights on Palmprint Identification for Faster and More Accurate Matching

This paper introduces a generalized palmprint identification framework to unify several state-of-art 2D and 3D palmprint methods. Through this framework, we argue that the methods employing one-to-one matching strategy and binary representation for feature are more effective for palmprint identification. The analysis for the first argument is based on a statistical matching model and is supported by outperforming results on several publicly available 2D palmprpint databases. These two arguments are further evaluated for 3D palmprint matching and used to introduce a new method for encoding 3D palmprint feature. The proposed 3D feature is binary and more efficiently computed. It encodes the 3D shape of palmprint to either convex or concave. The experimental results on two publicly available, from contactless and contact-base 3D palmprint database of 177 and 200 subjects, respectively, outperform the state-of-the-art methods. This paper also provides our palmprint matching algorithm(s) in public domain, unlike the previous work in this area, which will help to further advance research efforts in this area.