A comparative study of CFs, LBP, HOG, SIFT, SURF, and BRIEF techniques for face recognition

Yassin Kortli; Maher Jridi; Ayman Al Falou; Mohamed Atri

doi:10.1117/12.2309454

30 April 2018 A comparative study of CFs, LBP, HOG, SIFT, SURF, and BRIEF techniques for face recognition

Yassin Kortli, Maher Jridi, Ayman Al Falou, Mohamed Atri

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 106490M (2018) https://doi.org/10.1117/12.2309454
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

In recent years, developing Surveillance Systems (SS) for security has been one of the most active research fields in most applications. These systems used to adjust, enhance, and improve the security. One of these systems, face recognition system plays an efficient and very important tool for several applications despite the existence of different surveillance systems, like hand geometry, iris scan, as well as fingerprints. This is because it is natural, non-intrusive, and inexpensive. For the past two decades, various face recognition methods have been proposed to reduce the amount of calculation and improve the recognition rate. These proposed methods could be categorized into three significant categories: Local feature approaches, Subspace learning approaches, and Correlation filters approaches. In this paper, we discuss and compare some common face recognition algorithms. Our objective of this work is to demonstrate the effectiveness and feasibility of the best methods for face recognition in terms of design, implementation, and application.

Citation Download Citation

Yassin Kortli, Maher Jridi, Ayman Al Falou, and Mohamed Atri "A comparative study of CFs, LBP, HOG, SIFT, SURF, and BRIEF techniques for face recognition", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490M (30 April 2018); https://doi.org/10.1117/12.2309454

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