This paper introduces a novel approach to hand geometry-based identification.
The increased demand for tighter border and building security has renewed public interest in biometric identification and verification systems. With fingerprint recognition being socially stigmatized, hand geometry-based recognizers have emerged as niche solutions; however, systems currently available in the marketplace require direct contact with the device, raising, among other issues, significant hygiene concerns. The proposed method employs Active Appearance Models to track the hand inside the capture device and to extract geometry features for identification. The AAM fitting algorithm runs faster than real-time, enabling robust system performance. In experiments on a small-scale database of hand images, the accuracy of the authors’ system exceeds 90%, using as little as five features. (Publisher abstract provided)