Lee et al. / J Zhejiang Univ-Sci C (Comput & Electron) 2010 11(7):514-524 514 Finger vein recognition using weighted local binary pattern code based on a support vector machine * Hyeon Chang LEE 1 , Byung Jun KANG 2 , Eui Chul LEE 3 , Kang Ryoung PARK †‡1 ( 1 Division of Electronics and Electrical Engineering, Dongguk University, Seoul 100-715, Korea) ( 2 Electronics and Telecommunications Research Institute, Daejeon 305-700, Korea) ( 3 Division of Fusion and Convergence of Mathematical Sciences, the National Institute for Mathematical Sciences, Daejeon 305-340, Korea) † E-mail: parkgr@dongguk.edu Received Sept. 5, 2009; Revision accepted Feb. 1, 2010; Crosschecked June 9, 2010 Abstract: Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method. Key words: Finger vein recognition, Support vector machine (SVM), Weight, Local binary pattern (LBP) doi:10.1631/jzus.C0910550 Document code: A CLC number: TP391 1 Introduction As security requirements increase, biometric techniques, including face, fingerprint, iris, voice, and vein recognitions, have been widely used for personal identification (Jain et al., 2004). Biometrics has been applied to building access control, immigration con- trol, and user authentication for financial transactions. Although vein recognition has not been as widely adopted as fingerprint, face, and iris recognition, it has some advantages. Since vein patterns exist inside the skin, it is very difficult to steal them. In addition, the vein patterns are not easily altered by other factors such as dry or wet skin (Wang and Leedham, 2006; Watanabe, 2008). Retina vein recognition is one form of vein pattern recognition, which identifies a user based on retinal vascular patterns. Since a view of the retinal veins is contained within the pupil, a special camera device is required to obtain the vein patterns and the user must place his/her eye close to the cam- era (Usher et al., 2008; Sukumaran and Punithavalli, 2009). Vein patterns on the back of the hand have been adopted for matching in hand vascular identifi- cation (Ding et al., 2005; Ferrer et al., 2009). Palm vein recognition, investigated in previous studies, uses vein patterns in the palm for personal identifica- tion (Lin and Fan, 2004; Watanabe, 2008). Hand and palm vein recognitions, however, have a disadvantage Journal of Zhejiang University-SCIENCE C (Computers & Electronics) ISSN 1869-1951 (Print); ISSN 1869-196X (Online) www.zju.edu.cn/jzus; www.springerlink.com E-mail: jzus@zju.edu.cn ‡ Corresponding author * Project (No. R112002105070020(2010)) supported by the National Research Foundation of Korea (NRF) through the Biometrics Engi- neering Research Center (BERC) at Yonsei University © Zhejiang University and Springer-Verlag Berlin Heidelberg 2010