Finger Vein Recognition Using Pattern Map As Feature Extraction - Teoh, Saw Beng (2012)
Today, finger vein has become a new biometric technology. The challenge of finger vein recognition comes to during the process of feature extraction. Since the low contrast finger vein images may contain shading and noise, it is important to precisely preprocess, extract and preserve the vein patterns. Algorithms such as Gabor Filter, Local Line Binary Pattern (LLBP) and Principal Component Analysis (PCA) have been proposed in recent study to extract finger vein features. In this thesis, a modified pattern map feature extraction method is proposed for finger vein recognition. Instead of obtaining fmger vein features from multi-filtered images, the features images are generated from pattern templates which are the eigenveins obtained from PCA process. Every fmger vein image is then transformed into pattern map images from a pattern matching process between an input finger vein image and the pattern templates. Finally, nearest neighbour classifier with Euclidean distance metrics is used for classification. The main contribution of this thesis is the new way of generating pattern templates, which selects small blocks from every class within an area of constraint. Additionally, pattern map is implemented in finger vein recognition for the first time. Experimental results show that the proposed pattern map algorithm has better performance with a consistent identification rate of 99% and above compared to existing methods such as PCA and Gabor with FVCode. This shows that pattern map is a reliable feature extraction method and is able to represent finger vein pattern effectively.