Contactless palmprint verification based on SIFT and iterative RANSAC

Abstract

Contactless palmprint recognition is an effective technology for improving the user friendliness of palmprint recognition. The main challenge of contactless palmprint recognition is the intra-class variations due to the hand deformation caused by contactless image acquisition. Traditional palmprint feature extraction and matching algorithms usually require that the query image is well aligned with the gallery image, which cannot be always assured in contactless occasions. In this work, the scale invariant feature transform (SIFT) is applied for contactless palmprint feature extraction and matching. SIFT features are invariant to image rotation, translation, and scale variations, and hence are promising to solve the deformation problem. Moreover, an iterative RANSAC algorithm is proposed to refine the matched SIFT points. The iterative RANSAC retains more matched SIFT points than the traditional RANSAC algorithm despite the points comply with different transformation models. Experiments show a great improvement of verification accuracy on a public contactless palmprint database.