Extended Local Binary Pattern Features based Face Recognition using Multilevel SVM Classifier
Sujay S N1, H S Manjunatha Reddy2
1Sujay S N, Department, of Global Academy of Technology, Bengaluru, under VTU, Belagavi.
2H S Manjunatha Reddy, Professor and Head in Department of Electronics and communication engineering, Global Academy of Technology, Bengaluru. Karnataka, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 4123-4128 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5481098319/2019©BEIESP | DOI: 10.35940/ijrte.C5481.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Face recognition method is one of the authoritative biometric system in recognition methods to recognize the individual, because face is a distinctive biometric trait of an human being and it is the superior method of recognition. This paper proposes a novel Face recognition method by using extended LBP features. The pre-processing is carried out to extract the face area using viola-johns algorithm and all images are resized to 100×100. The LBP operator is applied on resized face images by rotating the each image by 15 degrees, i.e., at 7 degree left and 7 degree right and at zero degree to extract the feature vectors and final features are obtained by applying histogram technique. The SVM classifier is used for matching the database images with test images to measure the performance such as TSR, FAR, FRR & EER. The performance parameters are compared with existing algorithms for YALE and FERET database.
Keywords: Local Binary Pattern (LBP), Face Recognition Technology (FERET), False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Support vector machine (SVM), Total Success Rate (TSR),
Scope of the Article: Pattern Recognition