Robust features fusion utilization for supervised palmprint recognition

Abstract

AbstractThis article proposes robust features fusion methodology for supervised palmprint recognition. The process of features fusion has been formulated according to the hybridization between robust morphological features of principal lines and the following features extractors: principal component analysis (PCA), transformed domain extractors, and invariant moments. The region of interest (ROI) has been extracted based on the identification of the valley points locations. Moreover, both morphological operations and edge detection have been applied in order to formulate the correct ROI. First, both length and slope of each extracted principal line have been estimated morphologically. Second, different features extraction techniques have been applied for the resultant ROI including (PCA), transformed domain, and invariant moments. Finally, seven supervised machine learning methods have been manipulated in order to achieve the highest palmprint recognition accuracy. The accuracy of the recognition was evaluated by measuring sensitivity, dice, precision, Jaccard coefficients, correlation, accuracy, and recognition time. Experimental findings show that, among all tested machine learning methods, the feed forward neural network with back propagation based on features vector of the fusion between invariant moments and principal lines morphology has achieved approximately 99.9% of successful palmprints recognition.