Comparison of principal component analysis algorithm and local binary pattern for feature extraction on face recognition system

Abstract

Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. The result of the research shows that Local Binary Pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the Local Binary Pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.

Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

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