Abstract
Facial recognition has always gone through a consistent research area due to its non-modelling nature and its diverse applications. As a result, day-to-day activities are increasingly being carried out electronically rather than in pencil and paper. Today, computer vision is a comprehensive field that deals with a high level of programming by feeding the input images/videos to automatically perform tasks such as detection, recognition and classification. Even with deep learning techniques, they are better than the normal human visual system. In this article, we developed a facial recognition system based on the Local Binary Pattern Histogram (LBPH) method to treat the real-time recognition of the human face in the low and high-level images. We aspire to maximize the variation that is relevant to facial expression and open edges so to sort of encode edges in a very cheap way. These highly successful features are called the Local Binary Pattern Histogram (LBPH).
Highlights
Among other biometric methods, face recognition is one of the ways to identify any individual subject
The goal of face detection is to detect [15] and locate faces in the image, to extract human face to use in other areas
Local Binary Pattern Histogram (LBPH) algorithm is the combination of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) descriptor
Summary
Face recognition is one of the ways to identify any individual subject. To perform facial recognition, the system must position the face earlier in the input image or video stream. This step is called face acquisition or detection. The main face recognition methods are described. T. Kanade [2] completed an innovative study on automatic face recognition by implementing conventional features. Kanade [2] completed an innovative study on automatic face recognition by implementing conventional features His system achieved higher results with 75% accuracy on a data set of 20 subjects, and each subject took two photos, one for the model and one for the test.
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