Abstract
Objectives: Automatic face recognition has been an important area of biometric authentication and verification system in various applications including crime detection, access control, video surveillance, tracking service and other related areas. Methods/Statistical analysis: In this study, we present Grey Level Co-occurrence Matrix (GLCM) over Local Binary Patterns (LBP) named as GOL texture feature technique for face classification. The experiments have been conducted on AT & T Cambridge Laboratory face images also called (ORL-faces) and Georgia Tech (GT-faces) databases respectively. Findings: We performed a comparative analysis of GLCM and LBP method separately and results showed that the proposed GOL method outperformed in terms of average sensitivity, average specificity, and retrieval time. These findings show efficacy of our proposed system. Keywords: GLCM; LBP; Face recognition; Feature extraction
Highlights
Since last two decades, automated face recognition remains a most challenging research area in the field of computer vision, human computer interaction and pattern recognition
4.1 Face image testing on Georgia Tech (GT) dataset A query face image is given as input to Grey Level Co-occurrence Matrix (GLCM) method
Feature extraction remains most challenging in the context of face recognition since last few decades
Summary
Since last two decades, automated face recognition remains a most challenging research area in the field of computer vision, human computer interaction and pattern recognition. Until now various face recognition systems have been suggested by different researchers. These systems fail to recognize human face in uncontrolled environment, such as small changes due to illumination, pose variation, facial expression and view point[5]. It is difficult to develop a face recognition system which could provide high accuracy with a good retrieval of time. A face recognition system is divided into three modules: 1) image capture, 2) feature extraction and 3) classification.
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