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Palm print is one of the modalities that offer high recognition accuracy. The recognition process depends on an optimized ROI (Region of Interest) extraction. This extraction is affected by several factors including the device used and the acquisition conditions. The acquisition mode can alter some image properties like rotation, translation and scale. Some devices are designed to maintain hand in a fixed position and delimit a subspace of the hand. On the other hand, contactlessdevices offer more convenience and flexibility but lead to altered images. ROI extraction methods must consider the acquisition device (with contact or contactless). In this paper, we propose a ROI extraction method that addresses this issue.We test our method on two databases PolyU and CASIA which illustrate the impact of using contactless device unlike the PolyU device. Then, we test performances of the palmprint biometric system. We use a Fisher Linear Discriminant projection (FLD) to extract features from ROI transformed into the frequency domain. Our proposed method can significantly cover a great portion of the palm in the two databases.Performances obtained with the proposed palmprint system are promising.

A multi‐biometric system uses different modalities to identify individuals more accurately. The authors analyse fusion efficiency of a significant number of multi‐biometric fusion schemes. To do so, the study applies different functions that are generated using genetic programming (GP) on the 2000 multi‐biometric instances produced by the fusion of different biometric matching scores. The functions are represented using a tree of arithmetic operations and are used for fusion at score level. First, genetic programming is implemented on the XM2VTS score database. The GP optimizes the half total error rate of fused matching scores. Then, a comparative study is performed based on our experiments on matching scores of different biometric baseline systems provided by the bio‐secure database. This database provides 24 streams that we use to generate 2000 multi‐biometric combinations. These multi‐biometric instances combine matching scores of different instances, sensors and traits. To assess the quality of the fused scores and the quality of performing biometric baseline systems, we use weighted functions based on user‐specific and group‐specific normalization. Then, we propose a hybrid cat swarm optimization (CSO) based on the average‐velocity inertia‐weighted CSO and the normal mutation strategy‐based CSO to compute the weights of the selected functions for the fused biometric systems. Finally, we present the statistical significance tests to confirm that the proposed functions outperform the existing functions based on arithmetic rules, normalization fusion and evolutionary algorithms.

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