3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier

Abstract

Since past decade, efforts are afoot to design better hand-based automatic person recognition systems. Among the various hand-based biometric traits, palmprint as a biometric characteristic is now gaining increased attention from both the academic and industrial communities owing to its highly distinctive texture patterns, features richness, and stability. Here, the authors propose a new 3D palmprint recognition framework based on an unsupervised convolutional deep learning network named PCANet. Specifically, the proposed framework first reconstructs illumination-invariant 3D palmprint images using Single Scale Retinex (SSR) algorithm. Then, PCANet topology is employed to extract discriminative features from SSR images. Finally, a multi-class support vector machine (SVM) classification scheme is utilised to determine the identity of the person. Extensive experimental analysis on publicly available 3D palmprint PolyU dataset, which is composed of 8000 range images from 200 individuals, shows that proposed method outperforms existing approaches and is also able to attain 99.98% rank-1 accuracy.