Probabilistic fusion of regional scores in 3D face recognition
Information fusion in biometrics mostly relates to multi-biometric systems which attempt to improve the performance of individual matchers: multi-sensor, multi-algorithm, multimodal, etc. However, in addition to these scenarios, the need for methods to fuse the individual regional classifiers has also emerged, due to the increasing number of region-based methods proposed to overcome expression and occlusion problems in face recognition. In this paper, we present a combination approach by converting the regional match scores into probabilities with the help of estimated regional confidence measures. Initially, face is broken into several segments and similarity and confidence scores are obtained. Then, the posteriori probabilities of the user being genuine are calculated in each region given these two scores. For this calculation, the conditional densities are obtained on the training samples by applying non-parametric kernel density estimation separately for different intervals of confidence levels. Experimental results demonstrate that the inclusion of the regional confidence measures via probabilistic conversion is much more advantageous when compared to weighted sum of original scores.