In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).

This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.

In this paper, we propose an algorithm for finger vein recognition with less complexity in the image preprocessing phase, where finger vein pattern extraction is not included at all. In the proposed algorithm, we implement phase-only correlation (POC) function at the matching stage with a very simple preprocessing technique. Experimental evaluation of the proposed algorithm using a set of finger vein images captured from a low cost device have resulting an efficient recognition performance where the equal error rate (EER) was 0.9803% with a total processing time of 0.6362s.

Sparse representation classification (SRC) is one of the popular methods of classification in biometrics, in which the decision of class for the test sample was based on the class with minimum reconstruction error. As SRC is based on the sparsity of the images, a decision based on reconstruction error is not ideal. In this study, an efficient classification methodology for finger vein recognition called mutual SRC (MSRC) is proposed. MSRC classifies the test sample by a new decision rule which significantly improves the recognition rate of the conventional SRC. By this new decision rule, the classification of the test sample is not only based on the nearest sparse neighbour but also on determining the training sample which considers the test sample as its nearest neighbour (NN). In this method, the training set is selected based on reconstruction error for the test sample, then which training sample considers the test sample as its NN is identified by sparse representation. Increases of 4.67, 10.59, 26.82, and 3.44% in the recognition rates are observed for the proposed MSRC method when compared with conventional SRC using the four public finger vein database.