Multiple features and classifiers for vein based biometric recognition

Abstract

An effective fusion scheme is necessary for combining features from multiple biometric traits. This paper presents a method of fusion using multiple features from hand vein biometric traits for Multimodal biometric recognition. In the proposed method, a biometric authentication system using three different set of veins images, such as, finger vein, palm vein and dorsal vein is developed. Here the multiple features from the input vein images are extracted by applying Radon transform, Hilbert-Huang transform and Dual tree complex wavelet transform for each of the vein images. Once the features are extracted, a feature level fusion is carried out using the optimization algorithm called, Group Search Optimization. Then, recognition is done using the trained features by different classifiers such as support vector machine, fuzzy, neural network, bayes classifier and k-nearest neighbor classifiers. This approach is tested on the standard data bases of finger vein, palm vein and dorsal vein images of the hand. The proposed method provides higher accuracy and lower equal error rate which shows the efficiency of the technique compared with the other existing techniques.

Improved methods for finger vein identification using composite Median-Wiener filter and hierarchical centroid features extraction

“…Compared to a single extraction type of feature, the fusion of many features of extraction can improve the performance of the finger vein identification system [6]. Therefore, it was found that the areas for a combination of multiple features in the feature extraction stage for the finger vein identification system need to be explored more [49].…”

Performances Enhancement of Fingerprint Recognition System Using Classifiers

“…The recognition is done using different classifiers such as support vector machine (SVM), neural network, fuzzy, bayes classifier and k-nearest neighbour (KNN) classifiers. An accuracy above 90% was reported for all used classifies except Navie bayes classifier which provides the accuracy of around 80% which is low as compared to used classifiers [6].…”

Feature Extraction for Finger-Vein-Based Identity Recognition

“…In 2018, Subramaniam and Radhakrishnan [ 89 ] developed a biometric authentication system that uses finger, palm, and dorsal vein images. After preprocessing the image, the feature extraction was performed by applying three different transformations: Hilbert–Hung, Radon, and Dual-Tree Wavelet Transform.…”

“…J. Imaging 2021, 7, 89 2 of 30 that they remain unchanged over time and can be measured without subjecting the human to a painful process. The finger vein trait satisfies in some degree the seven factors [1] that define the suitability of a biometric trait in order to be useful for identity authentication: (1) Universality, (2) Uniqueness, (3) Permanence, (4) Measurability, (5) Performance, (6) Acceptability, and (7) Circumvention.…”