Face Recognition Using HMM-LBP

Chihaoui, Mejda; Bellil, Wajdi; Elkefi, Akram; Amar, Chokri Ben

doi:10.1007/978-3-319-27221-4_21

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…Kar et al [63] used Gabor Wavelet for feature extraction, and the Hidden Markov Model for classification, and reported 81.25% accuracy in 2013. As a recent study, Chihaoui et al [64] applied a local binary pattern algorithm for feature extraction, and the Hidden Markov Model for recognition in 2016. The success rate of this study was 99%.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

2018

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Biometry based authentication and recognition have attracted greater attention due to numerous applications for security-conscious societies, since biometrics brings accurate and consistent identification. Face biometry possesses the merits of low intrusiveness and high precision. Despite the presence of several biometric methods, like iris scan, fingerprints, and hand geometry, the most effective and broadly utilized method is face recognition, because it is reasonable, natural, and non-intrusive. Face recognition is a part of the pattern recognition that is applied for identifying or authenticating a person that is extracted from a digital image or a video automatically. Moreover, current innovations in big data analysis, cloud computing, social networks, and machine learning have allowed for a straightforward understanding of how different challenging issues in face recognition might be solved. Effective face recognition in the enormous data concept is a crucial and challenging task. This study develops an intelligent face recognition framework that recognizes faces through efficient ensemble learning techniques, which are Random Subspace and Voting, in order to improve the performance of biometric systems. Furthermore, several methods including skin color detection, histogram feature extraction, and ensemble learner-based face recognition are presented. The proposed framework, which has a symmetric structure, is found to have high potential for biometrics. Hence, the proposed framework utilizing histogram feature extraction with Random Subspace and Voting ensemble learners have presented their superiority over two different databases as compared with state-of-art face recognition. This proposed method has reached an accuracy of 99.25% with random forest, combined with both ensemble learners on the FERET face database.

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Section: Discussionmentioning

confidence: 99%

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

2018

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“…• HMM-LBP [80]: This is a hybrid approach called HMM-LBP permitting the classification of a 2D face image by using the LBP tool (local binary pattern) for feature extraction. It consists of four steps.…”

Section: Hybrid Approaches and Methods Based On Statistical Modelsmentioning

confidence: 99%

“…It consists of four steps. First, [80] decomposes the face image into blocs. Then, this approach extracts image features using LBP.…”

Section: Hybrid Approaches and Methods Based On Statistical Modelsmentioning

confidence: 99%

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A Survey of 2D Face Recognition Techniques

et al. 2016

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Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method's principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

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“…The disadvantage is that it requires more computational power. Chihaoui et.al [14]: proposed a hybrid approach called HMM-LBP permitting the classification of a 2Dface image by using the LBP tool (local binary pattern) for feature extraction. It consists of four steps.…”

Section: IVmentioning

confidence: 99%

A Review on Face Recognition Techniques

Daniel¹

2018

IJRASET

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In spite of various biometric techniques like fingerprints, iris scan as well as hand geometry, the most efficient and more widely used one is face recognition. Face Recognition plays a major role in Biometrics. Face recognition is computer application capable of identifying or verifying a person from the image or video source. In comparison with other biometrics, the face has some main advantages such as good acceptability, low cost, and contactless acquisition. Face Recognition has received a great attention over the last few years because of it has many applications in various fields. In spite of rapid progress in this field it still has to overcome various challenges like Aging, Partial Occlusion, and Facial Expressions etc affecting the performance of the system. This paper reviews the basics of face recognition system, its recent techniques and challenges.

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Face Recognition Using HMM-LBP

Cited by 8 publications

References 11 publications

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

A Survey of 2D Face Recognition Techniques

A Review on Face Recognition Techniques

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