Palmprint Recognition Based on Subspace and Texture Feature Fusion

Aiming at the problem of low recognition rate because the single descriptor cannot accurately obtain the effective palmprint features, a palmprint recognition method is proposed based on subspace and texture feature fusion. The subspace feature and texture feature of a palmprint image arc obtained by robust linear discriminant analysis and local direction binary pattern, respectively. The weighted concatenation method is used for the subspace and texture feature fusion. The chi-square distance among the fused feature vectors is used for identification matching. The experimental results on the PolyU and the self-built non-contact databases show that the recognition time is 0. 3069 s and 0. 3127 s, respectively, and the lowest equal error rate is only 0. 3140% and 1.1922%, respectively. Compared with other methods, the proposed method can accurately obtain the effective feature information of a palmprint image and improve the system recognition performance under the premise that the real-time performance is ensured.