Signal, Image and Video Processing https://doi.org/10.1007/s11760-020-01765-6 ORIGINAL PAPER Palm vein recognition through fusion of texture-based and CNN-based methods Felix Olanrewaju Babalola 1 · Yıltan Bitirim 1 · Önsen Toygar 1 Received: 14 February 2020 / Revised: 13 July 2020 / Accepted: 10 August 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020 Abstract In this paper, we propose a palm vein recognition system that combines two approaches using a decision-level fusion strategy. The first approach employs Binarized Statistical Image Features (BSIF) descriptor method on five overlapping sub-regions of palm vein images and the second approach uses a convolutional neural networks (CNN) model on each palm vein image. In the first approach, texture-based features of five overlapping sub-regions on the palm vein image are extracted using the powerful BSIF method and the scores obtained by the matching step of the system are fused with score-level fusion strategy. In the second approach, a CNN model is used to train the system using the whole image. Afterwards, the decisions of two approaches are gathered separately and a final decision is obtained by fusing the two decisions. Experimental results on CASIA, FYO, PUT, VERA and Tongji Contactless Palm Vein databases showed that the proposed method compared favorably against other similar systems. Keywords Biometrics · Hand vein · Feature extraction · Data fusion · Palm vein recognition 1 Introduction Nowadays, biometric systems are among the most widely used technologies in the world for person authentication. The development of new technologies that effectively cap- tures these biometric traits have also benefited their use for human recognition. Many research studies in this area include unimodal and multimodal biometric traits for per- son identification or verification [1,2]. Among these systems, face, iris, fingerprint, palmprint, gait and voice are the most widely used biometric traits. However, the new trend is the usage of vein images captured from finger, palm and other regions of the hand such as wrist. Palm vein as biometric trait has received enormous atten- tion in recent years because it is located under the skin which makes it relatively impossible to spoof. It is also relatively stable and devoid of occlusion and noise such as hair. The development of low cost devices capable of capturing palm vein patterns has also made it popular for use in high security B Önsen Toygar onsen.toygar@emu.edu.tr 1 Computer Engineering Department, Faculty of Engineering, Eastern Mediterranean University, Famagusta 99628, North Cyprus, via Mersin 10, Turkey authentication systems. The image capturing process is fast and user friendly which makes it easy for users to willingly use such devices. In this paper, we concentrated on palm vein as a biometric trait and propose a method for improving palm vein biometric authentication systems by combining a texture-based method with a convolutional neural networks (CNN)-based method. The first method extracts features from five overlapping sub-regions of palm vein images using Binarized Statistical Image Features (BSIF) which is a state-of-the-art texture- based algorithm. The BSIF method obtains binary codes for neighborhood of each pixel of an image by binarizing fil- ter responses that are generated by convolving through the image using a set of linear filters [3]. The features extracted by BSIF are matched and fused by score-level fusion strat- egy. The second method is a deep learning CNN architecture based on AlexNet structure [4] which was trained to obtain the second decision. The final decision of the proposed sys- tem is obtained by fusing the decisions of the two structures, namely BSIF with 5 sub-regions and CNN model. The experiments were conducted on CASIA [5], FYO [6], PUT [7], VERA [8] and Tongji [9] databases to demonstrate the results for the proposed method. Samples of palm vein images from each dataset are shown in Fig. 1 with their corre- sponding Region of Interest (ROI) images. Additionally, we 123