WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 12, 2013
Combined SIQT and SSF Matching Score for Feature Extraction Evaluation in Finger Knuckle Print Recognition
Authors: ,
Abstract: Feature selection has been a prevailing part of examine in biometrics, image reclamation, data mining, and text classification. The major design of feature selection is to choose a set of features, by removing the irrelevant as well as the surplus features that are robustly associated. Many classification techniques have been presented for feature extraction process but there has bee no effort for feature selection. The previous work used texture and color intensive biometric (TCIB) for multimodal security that achieves significant presentation even for the huge pose variations with various angles. The matching pattern is done using texture values but the outcome of the image is not as clear as much. To improve the knuckle finger print recognition system, in this paper, we present a narrative grouping of restricted information for a proficient finger-knuckle-print (FKP) based recognition system which is vigorous to extent and rotation. The non-uniform clarity of the FKP due to comparatively curvature surface is accurate and texture is improved. The features of the improved FKP are mined using the Scale Invariant Quality Transform (SIQT) and the Strong Speeded up features (SSF). Consequent features of the register and the query FKPs are coordinated using nearest-neighbor-ratio method and subsequently the consequent SIQT and SSF matching scores are combined using weighted sum rule. An experimental result are carried on the datasets with seven sample images in a substantial pose variations provides enhanced results compared to an existing Texture and Color intensive biometric multimodal security using hand geometry and palm print. The proposed system is evaluated with the set of images for both classification and authentication mode. It is practical that the system achieves with CRR of 100% and EER of 0:215%.
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Keywords: Palm print, Hand Geometry, Biometrics, Feature selection, Knuckle finger feature selection, SIQT, SSF, TCIB