Comparison of Haar-like, HOG and LBP approaches for face detection in video sequences
Amal Adouani, Wiem Mimoun Ben Henia, Zied Lachiri · 2019
Face detection is an essential part of any face recognition system as a first step to detect faces. This paper presents a comparative study of three commonly used approaches for face detection, namely Haar-like cascade, Histogram of Oriented Gradients with Support Vector Machine and Linear Binary Pattern cascade. For this aim, video sequences from the Database for Emotion Analysis using Physiological Signals (DEAP) were explored. The proposed methods were developed using Python language with OpenCV and Dlib libraries. The obtained results show that HOG+SVM approach is more robust and accurate than LBP and Haar approaches with an average detection rate of 92.68%.