Feature selection for the fusion of face and palmprint biometrics

Multimodal biometric systems aim to improve the recognition accuracy by minimizing the limitations of unimodal systems. In this paper, different fusion schemes based on feature-level and match score-level fusion are employed to provide a robust recognition system. The proposed method presents a multimodal approach based on face–palmprint biometric systems by match score-level fusion technique. Local binary patterns are performed as local feature extractor to obtain efficient texture descriptor. Feature selection is performed using backtracking search algorithm to select an optimal subset of face and palmprint extracted features. Hence, computation time and feature dimension are considerably reduced while obtaining the higher level of performance. Then, match score-level fusion is performed to show the effectiveness and accuracy of the proposed method. In score-level fusion, face and palmprint scores are normalized using tanh normalization and matching scores of individual classifiers are fused using sum rule method. The experimental results are tested on a developed virtual multimodal database combining FERET face and PolyU palmprint databases. The results demonstrate a significant improvement compared with unimodal identifiers, and the proposed approach significantly outperforms other face–palmprint multimodal systems with a recognition accuracy of 99.17 %. Additionally, the proposed approach is compared with the state-of-the-art methods.