Open AccessA Robust Texture Based Ear and Palmprint Recognition Using Histogram of Oriented Gradients
Author(s) -
K. Naga Prakash,
P Hema,
Dr.K. Prasanthi Jasmine
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8558.088619
Subject(s) - palm print , histogram , artificial intelligence , biometrics , computer science , identification (biology) , pattern recognition (psychology) , local binary patterns , feature (linguistics) , fingerprint (computing) , palm , computer vision , image (mathematics) , linguistics , philosophy , botany , biology , physics , quantum mechanics
Recent research in the surface-based ear and palm print recognition additionally shows that ear identification and palm print identification. The surface-based ear and palm print recognition are strong against sign corruption and encoding antiques. Based on these discoveries, further research and look at the comparison of surface descriptors for ear and palm print recognition and try to investigate potential outcomes to supplement surface descriptors with depth data. The proposed Multimodal ear and palm print Biometric Recognition work is based on the feature level fusion. Based on the ear images and palm print images from noticeable brightness as well as profundity records, we remove surface with outside labels starting complete contour images. In this paper, think about the recognition performance of choose strategies for describing the surface structure, which is Local Binary Pattern (LBP), Weber Local Descriptor (WLD), Histogram of oriented gradients (HOG), and Binarised Statistical Image Features (BSIF). The broad test examination dependent scheduled target IIT Delhi-2 ear and IIT Delhi palm print records affirmed to facilitate and expected multimodal biometric framework can build recognition rates contrasted and that delivered by single-modular for example, Unimodal biometrics. The proposed method Histogram of Oriented Gradients (HOG) achieving a recognition rate of 124%