Abstract
Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.
Highlights
Hand based biometric identification is a booming research area due to its low cost in acquiring data, its reliability in identifying individuals and its degree of acceptance by the user [1]
The texture design obtained by bending the finger knuckle of a person is unique and that can be used as a biometric trait
Morales proposed Finger Knuckle Print (FKP) authentication based on Scale Invariant Feature Transform (SIFT) algorithm [6]
Summary
Hand based biometric identification is a booming research area due to its low cost in acquiring data, its reliability in identifying individuals and its degree of acceptance by the user [1]. Finger knuckle print is a newly emerged biometric trait in recent years. The texture design obtained by bending the finger knuckle of a person is unique and that can be used as a biometric trait. Recently proposed local binary pattern based descriptors such as LDN, LDTP and LTDP_MLDN are used to extract features from FKP images. Morales proposed FKP authentication based on Scale Invariant Feature Transform (SIFT) algorithm [6]. Multi-resolution methods provide powerful signal analysis tools, which are widely used in feature extraction, recognition, denoising, compression, detection and image retrieval applications. These techniques are used by Ekenel and Sankur [9] to reduce the loss in FKP recognition. A new combination of Local Binary Pattern derivatives in Contourlet domain is evaluated in this paper
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