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All Journal International Journal of Robotics and Control Systems
Taraneh Kamyab
University of North Carolina at Charlotte
Control & Systems Engineering Electrical & Electronics Engineering

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Comparison and Review of Face Recognition Methods Based on Gabor and Boosting Algorithms Taraneh Kamyab; Alireza Delrish; Haitham Daealhaq; Ali Mojarrad Ghahfarokhi; Fatemehalsadat Beheshtinejad
International Journal of Robotics and Control Systems Vol 2, No 4 (2022)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v2i4.759

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 Taraneh Kamyab; Haitham Daealhaq; Ali Mojarrad Ghahfarokhi; Fatemehalsadat Beheshtinejad; Ehsan Salajegheh
International Journal of Robotics and Control Systems Vol 3, No 1 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i1.849

Abstract

Face recognition methods are computational algorithms that follow aim to identify a person's image according to the bank of images they have of different people. So far, various methods have been proposed for face recognition, which can generally be divided into two categories based on face structure and based on facial features. Based on this, many algorithms have been introduced and used for face recognition. Genetic algorithm has been one of the successful algorithms for face recognition. In this article, we first briefly explained the genetic algorithm and then used the combination of neural network and genetic algorithm to select and classify facial features The presented method has been evaluated using individual features and combined features of the face region. Composite features perform better than face region features in experimental tests. Also, a comprehensive comparison with other facial recognition techniques available in the FERET database is included in this paper. The proposed method has produced a classification accuracy of 94%, which is a significant improvement and the best classification accuracy among the results established in other studies.
Co-Authors Ali Mojarrad Ghahfarokhi Alireza Delrish Ehsan Salajegheh Fatemehalsadat Beheshtinejad Haitham Daealhaq