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.

Combination of Genetic Algorithm and Neural Network to Select Facial Features in Face Recognition Technique

“…Advances in computing power over the past decades have made it possible to perform face recognition automatically. Early facial recognition algorithms used geometric models, but today's recognition processes use complex mathematical models for feature extraction [1]. Major advances in the last ten to fifteen years have led to the advancement of facial recognition technology.…”

Improving the Recognition Percentage of the Identity Check System by Applying the SVM Method on the Face Image Using Special Faces

“…Identification systems or identity checks through facial images are no exception to this rule [1]. Many algorithms for feature extraction have been reviewed and introduced, specifically their application in climate change and water treatment areas [2]- [6]. Support vector machine is one of the supervised learning methods used for classification and regression [7]- [9].…”

Smart home Management System with Face Recognition Based on ArcFace Model in Deep Convolutional Neural Network

“…AI consists of main applications as following: Natural language processing, computer vision [61,62], machine learning [63], data processing [64], and optimal decision. Following this current trend, we exploit a powerful deep learning approach in facial and hand gestures recognition applying for IoT control management systems [27][28][29][30][31][32][33][34][35][36][37][38][39]. Local database of identified people supports into both security and privacy.…”