Abstract
Palm-print and iris biometric traits fusion are implemented in this paper. The region of interest (ROI) of a palm is extracted by using the valley detection algorithm and the ROI of an iris is extracted based on the neighbor-pixels value algorithm (NPVA). Statistical local binary pattern (SLBP) is applied to extract the local features of palm and iris. For enhancing the palm features, a combination of histogram of oriented gradient (HOG) and discrete cosine transform (DCT) is applied. Gabor-Zernike moment is used to extract the iris features. This experimentation was carried out in two modes: verification and identification. The Euclidean distance is used in the verification system. In the identification system, the fuzzy-based classifier was proposed along with built-in classification functions in MATLAB. CASIA datasets of palm and iris were used in this research work. The proposed system accuracy was found to be satisfactory.
Highlights
Pattern recognition classifies data based on already gained knowledge [1], it is a process of understanding the class to which an object/pattern belongs
Comparison between unimodal and multimodal biometrics system based fuzzy classifier was done with Matlab
Statistical local binary pattern (SLBP) was the common technique to extract the local features of palm and iris
Summary
Pattern recognition classifies data based on already gained knowledge [1], it is a process of understanding the class to which an object/pattern belongs. Those patterns/objects can be 1D e.g. signals or 2D e.g. images. There are three types of biometrics: physical, behavioral and cognitive. Physical biometrics deal with the bodily parts like face, fingerprints, palm etc. Behavioral biometrics considers the activities of the body like gait, keystroke, and voice. Cognitive biometrics regard every human nervous tissue and its response to signals like electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and electrodermal response (EDR). Two physical biometrics have been considered (palm-print and iris). Verification One to one search Answer the question “Can you prove who you are?” ID is needed as manually input No need for classification Less time for verification
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