ABSTRACT

This paper proposes a novel technique for palmprint recognition in context to biometric identification of a person. Palmprints are images of the inner portion of a person’s palm and consist of a complex pattern of randomly oriented curves and lines. This random pattern can provide a unique identifier of a person if a mathematical model can be built to represent and compare it. In this paper the palmprint images are mapped to Eigen-space and a robust code signature is generated from different camera snapshots of the same palm to incorporate tonal and lighting variations. To enable real-time identification, the signature is represented by a low dimensional feature vector to reduce computational overheads. It is observed to produce high recognition accuracies which highlight reliability of the feature.