Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms

Abstract

The face plays an essential role in identifying people and showing their emotions in society. The human ability to recognize faces is remarkable. But face recognition is a fundamental problem in many computer programs. Due to the inherent complexities of the face and the many changes in its features, different algorithms for face recognition have been introduced in the last 20 years. Face recognition methods that are based on the structure of the face are unsupervised methods that produce good results compared to the linear changes that occur in the image. In this article, the Gabor algorithm, which is the origin of face recognition algorithms, has been described. Over the past decade, most of the research in the area of pattern classification has emphasized the use of the Gabor filter bank for extracting features. Because the Gabor algorithm has shortcomings, researchers have introduced a new method that is a combination of Gabor and PCA. After the introduction of the Gabor method, more complete and accurate algorithms have been introduced, such as Boosting algorithms, which we have briefly explained in this article. Also, here are the results of the comparison made by the researchers between Boosting and Gabor algorithms. The results show that Boosting-based algorithms have performed better compared to Gabor-based algorithms.