Multimodal biometric system combining left and right palmprints

Abstract

Purpose Palmprint recognition is a very interesting and promising area of research. Much work has already been done in this area, but much more needs to be done to make the systems more efficient. In this paper, a multimodal biometrics system based on fusion of left and right palmprints of a person is proposed to overcome limitations of unimodal systems. Design/methodology/approach Features are extracted using some proposed multi-block local descriptors in addition to MBLBP. Fusion of extracted features is done at feature level by a simple concatenation of feature vectors. Then, feature selection is performed on the resulting global feature vector using evolutionary algorithms such as genetic algorithms and backtracking search algorithm for a comparison purpose. The benefits of such step selecting the relevant features are known in the literature, such as increasing the recognition accuracy and reducing the feature set size, which results in runtime saving. In matching step, Chi-square similarity measure is used. Findings The resulting feature vector length representing a person is compact and the runtime is reduced. Originality/value Intensive experiments were done on the publicly available IITD database. Experimental results show a recognition accuracy of 99.17 which prove the effectiveness and robustness of the proposed multimodal biometrics system than other unimodal and multimodal biometrics systems.

Multimodal Biometrics Fusion Algorithm Using Deep Reinforcement Learning

“…Data transformation is to transform the value range of single-modal biometric image data according to demand so as to better implement the processing of single-modal biometric image data [8]. e processing method of data transformation is the minimum-maximum normalization method, which mainly implements the linear transformation of value range data through the linear transformation method [9] so that the transformed data is in a unified value range.…”

Guest editorial

“…But for images taken from low-cost devices, how to make the systems efficient and effective calls for some promising alternative biometric authentication. Taouche and Belhadef (2019) proposed to use multimodal biometrics by combining left and right palmprints of an individual into an authentication system. New feature descriptors, MB-LDP, MB-ELDP and MB-LDN are proposed, and then the GA and BSA are applied to do feature selection.…”