High performance set of features for biometric data

This paper focuses on the use of image-based techniques in biometric verification. A detailed review of the existing literature on texture descriptors is provided and several methods are compared on three well known biometric problems: palm verification, knuckle verification and fingerprint verification. The texture descriptors evaluated in this study are based on the most commonly used measures, i.e., Gabor filter bank response, local binary patterns, histogram of gradients, and local phase quantization. Moreover, different distance measures are compared for obtaining the best performing system. The most common method for handling biometric data is to determine a common set of optimal features and then apply standard machine-learning algorithms and distance measures to classify them. In this paper we use advanced supervised selection methods for determining an optimized set of features for training an ensemble of classifiers and for reducing the dimensionality of the feature set by discarding the less discriminative features. The optimization process requires that we first run several experiments to determine which feature set offers the most information. The best performing feature set is then combined and used in the ensemble classification. Extensive experiments conducted over the three well-known biometric datasets show that it is possible to find a set of descriptors that works well for all the three tasks. We are thus able to produce a set of optimal generalized features. The best tested method is local phase quantization.