Development of finger vein identification system using feature combination and dimensionality reduction - Ting, Ei Wei (2018)
With the increase in globalization and living standard, finger vein biometric trait becomes a popular focus in the human recognition and security system. Finger vein is high security as it is hard to be stolen or duplicate since it hides underneath the finger skin. Image quality and contrast is the main problem that was faced by researcher in the finger vein identification system. It may cause by the wrong posture, light intensity and image rotation during the image captured session. Previous works had stated that the wavelet-based features are invariant to image rotation while local binary based features robust against high saturation and irregular shading. However, the feature combination of these two different features type has not been studied yet in finger vein identification system. Besides that, the dimensionality reduction technique such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) was frequently involved in finger vein recognition system in this recent year. Generally, PCA was used to remove the noise residing in the discarded dimension. However, the features reduction in earlier stage using PCA may remove the important features as PCA might remove the local information where it concludes that the information for the corresponding eigenvalue is noise. Therefore, this research proposed to develop a finger vein identification using combination features of Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The combined features were then undergoing dimensionality reduction using PCA and LDA. The finger vein image firstly undergoes image pre-processing to enhance the image quality, followed by region of interest (ROI) extraction and noise removal using median filtering. The pre-processed finger vein images will go through feature extraction using DWT and LBP to get the features in vectors form and then combined. The process was continued by applying PCA or LDA on the combination features. Linear Support Vector Machine (SVM) and Fine K-Nearest Neighbours (FKNN) were used for classification in this research. At the same time, the conventional finger vein identification system was designed with Repeated Line Tracking (RLT) and Maximum Curvature (MAXC) as feature extraction methods followed by Speeded-up Robust Features (SURF) as the classification method. The performance of the proposed system was compared with the conventional finger vein identification system using same database which is known as SDMULA-HMT. Throughout the experiments, the feature combination of DWT and LBP was proved to perform better than the finger vein identification system that involved only single type of feature by average of 6.91% improvement. At the early stage, the proposed system achieved 80.16% identification rate, which is a bit lower than the conventional system. However, when the proposed system and the conventional system are applied with dimensionality reduction using PCA and LDA, the identification rate increased dramatically in proposed system while there was only small improvement on the conventional finger vein identification system. The proposed system achieved the highest identification rate at 100% when the feature is performed using LDA at feature dimension of 30.