Open AccessA Face Recognition System Using Directional Binary Code Algorithm And Multi-Svm
Author(s) -
M. Tamilselvi,
S. Karthikeyan
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.f1204.0986s319
Subject(s) - computer science , facial recognition system , artificial intelligence , pattern recognition (psychology) , local binary patterns , feature extraction , support vector machine , computer vision , face (sociological concept) , face detection , binary number , robustness (evolution) , filter (signal processing) , image (mathematics) , mathematics , histogram , social science , biochemistry , chemistry , arithmetic , sociology , gene
Over past few years, face recognition technology plays an important function in the development of biometric identifier with less time consuming and computational overhead. Many researchers were put their effort to develop face recognition algorithm involves three distinct steps such as detection, unique faceprint creation and finally verification. Traditional Local binary pattern based face recognition system slow down the recognition speed, high computational complexity and does not give the directional data of the picture. In order to overcome the above limitation, a novel face recognition system is proposed by employing the advantage of Directional Binary Code (DBC) feature extraction method. The face images features are extracted from DBC are generally smoother than other feature extraction methods. The images with blur creation, pose changes, and illumination is applied and stored in the database. For blur creation various filters such as Average filter, Gaussian filter and Motion filter are used. By using Directional Binary Code method, the face is detected and extracted. Then the same algorithm is used for input images and with help of Multi-SVM classifier multiple images in the database is compared and shows the matched images. Finally, simulation result shows the implemented results in term of its recognition speed and computation complexity.