Improved palmprint authentication using contactless imaging

Palmprint identification has emerged as one of the popular and promising biométrie modalities for forensic and commercial applications. In recent years the contactless system emerges as a viable option to address hygienic issues and improve the user acceptance. The presence of significant scale, rotation, occlusion and translation variations in the contactless palmprint images requires the feature extraction approaches which are tolerant to such changes. Therefore the usage of traditional palmprint feature extraction methods on contactless imaging schemes remains questionable and hence all/popular palmprint feature extraction methods may not be useful in contactless frameworks. This paper we systematically examine the issues related to the contactless palmprint authentication and presents performance evaluation on the two public databases. Our experimental results on more than 4300 images from two contactless databases suggests that the Scale Invariant Feature Transform (SIFT) features perform significantly better for the contactless palmprint images than the (most) promising Orthogonal Line Ordinal Features (OLOF) approach employed earlier on the more conventional palmprint imaging. Our experimental results further suggests that the combination of robust SIFT matching scores along with those from OLOF can be employed to achieve more reliable performance improvement. The achieved error rates show a good performance of these features in controlled and uncontrolled environments conditions with the error rates similar to other contact based approaches.