Multimodal Biometric Authentication System Using Hand Shape, Palm Print, and Hand Geometry

Abstract

Developing a multimodal biometric system based on single-shot imaging (SSI) has recently grown interested in researchers worldwide. A palm region basically enriches with most discriminative features like lines, shape, and geometry which can be easily clubbed and captured together. In this work, feature-level fusion of hand shape, geometry, and palm print features has been performed. The extracted palm ROI samples undergo certain rotation and illumination effects that limit the matching performance. ROI samples are first geometrically aligned and then transformed into illumination-invariant form using CS-LBP. Further, local key points of transformed ROI images are extracted using SURF descriptor. In addition to this, a set of novel geometrical and shape features have also been computed from the hand registered image. All the three set of features are concatenated, and then, the highly uncorrelated features are selected from the fused feature set using sub-pattern PCA for classification. The performance of the proposed multimodal system is found to be superior to each of individual modality as well as reported state-of-the-art systems.