An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores

Abstract

Abstract. This paper presents a novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation. The non-uniform brightness of the FKP due to relatively curvature surface is corrected and texture is enhanced. The local features of the enhanced FKP are extracted using the scale invariant feature transform (SIFT) and the speeded up robust features (SURF). Corresponding features of the enrolled and the query FKPs are matched using nearest-neighbour-ratio method and then the derived SIFT and SURF matching scores are fused using weighted sum rule. The proposed system is evaluated using PolyU FKP database of 7920 images for both identification mode and verification mode. It is observed that the system performs with CRR of 100% and EER of 0.215%. Further, it is evaluated against various scales and rotations of the query image and is found to be robust for query images downscaled upto 60% and for any orientation of query image.

A New Finger-Knuckle-Print ROI Extraction Method Based on Two-Stage Center Point Detection

“…In order to evaluate the performance of our method, we apply the EER as benchmark in biometric systems. And in terms of feature matching technique, we choose the Speeded-Up Robust Features (SURF) [19] method.…”

A practical person authentication system using second minor finger knuckles for door security

“…Table 1 shows a summary of researches on finger knuckle recognition. Most researches [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] focused on recognition algorithms for texture patterns of PIP joints and evaluated its performance using a public finger knuckle image database such as the PolyU FKP database [18]. The images in the PolyU FKP database are captured under the controlled conditions, since the subject puts his/her finger on fixed blocks in order to reduce the spatial variations and capture clear line features of a finger knuckle.…”

“…Effectiveness of such coding approaches have been demonstrated in iris recognition [20] and palmprint recognition [21]. Some researches [8][9][10] employed local feature descriptors such as SIFT and SURF, which are used in the field of computer vision. Another approach [5,12,15,17] employed BandLimited Phase-Only Correlation (BLPOC), which is an image matching technique using the phase components in 2D Discrete Fourier Transforms (2D DFTs) of given images [22].…”

Finger Knuckle Print Identification using Gabor Features

“…It is reported that the skin pattern on the finger-knuckle is highly rich in texture due to skin folds and creases, and hence, can be considered as a biometric identifier. Various advantages of using Finger Knuckle Print (FKP) include rich in texture features, easily accessible, contact-less image acquisition, invariant to emotions and other behavioral aspects such as tiredness, stable features and acceptability in the society [3]. Literature survey of related work has already been discussed in detail, in our earlier papers [11] [24].…”