A fuzzy connective score fusion technique for 2D and 3D palmprint authentication system

Palmprint recognition systems have been extensively studied over the past two decades because of their unique, accurate, and stable biometric features. Many researchers have investigated the two-dimensional (2D) palmprint recognition that contains texture information, but the 2D palmprint image does not contain three-dimensional (3D) depth information. To beat the limitations associated with a 2D palmprint recognition system; this paper proposes using both 2D and 3D palmprint features for a personal recognition system. The BSIF and GIST descriptors are utilized for feature extraction from the 2D and 3D palmprint, respectively. Then, the PCA + LDA technique is used to reduce the dimensionality of features vectors. Next, the matching process is done using the Cosine distance. Finally, a score-level fusion was applied to get a final matching score using a fuzzy connective method based on the linear combination of triangular norms (T-norms and T-conorms). Also, the proposed method is compared to several techniques of score fusion, including Sum, Min, Max, and Symmetric Sum based on triangular norms. The system was evaluated on the publicly available Hong Kong-PolyU 2D + 3D palmprint database, and the obtained results show the efficiency of the proposed system.