Designing a New Face Recognition System Robust to Various Poses
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Authors
Behzad Ghanavati
- Department of Electrical and Computer Engineering , Mahshahr Branch, Islamic Azad University, Mahshahr, Iran.
Abstract
Different scholars in the world design wide varieties of systems for automatic face recognition process. The face recognition process is dependent on different variables, such as the illumination and the different poses of the image. Therefore, face recognition process is still a fundamental issue in image processing. In this paper, we have developed a new method for face recognition based on ant colony algorithm. To assess the performance and effectiveness of the designed system, face images available in ORL database are used. The results obtained indicate that the proposed method for face recognition accuracy is about 97.3 percent. Besides, comparisons indicate that the performance of the proposed method compared to other methods enjoys a remarkable accuracy.
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ISRP Style
Behzad Ghanavati, Designing a New Face Recognition System Robust to Various Poses, Journal of Mathematics and Computer Science, 15 (2015), no. 1, 32-39
AMA Style
Ghanavati Behzad, Designing a New Face Recognition System Robust to Various Poses. J Math Comput SCI-JM. (2015); 15(1):32-39
Chicago/Turabian Style
Ghanavati, Behzad. "Designing a New Face Recognition System Robust to Various Poses." Journal of Mathematics and Computer Science, 15, no. 1 (2015): 32-39
Keywords
- Face detection
- Face Recognition
- Face poses Ant Colony Optimization Algorithm.
MSC
References
-
[1]
A. Pentland, B. Moghaddam, T. Starner, View-based and modular eigenspaces for face recognition, in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, (1994), 84–91.
-
[2]
T. Cootes, K. Walker, C. Taylor, View-based active appearance models, in Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, (2000)
-
[3]
V. Blanz, S. Romdhani, T. Vetter, Face identification across different poses and illuminations with a 3D morphable model, in Proc. 5th Int. Conf. Automatic Face and Gesture Recognition, (2002), 202–207.
-
[4]
T. Cootes, G. Edwards, C. Taylor, Active appearance models, in Proc. Eur. Conf. Computer Vision, 2 (1998), 484–498.
-
[5]
V. Blanz, T. Vetter, Face recognition based on fitting a 3D morphable model, IEEE Trans. Pattern Anal. Mach. Intell., 25 (2003), 1063–1074.
-
[6]
I. Matthews, S. Baker, Active appearance models revisited, Int. J. Comput. Vis., 60 (2004), 135–164.
-
[7]
P. Kakumanu, S. Makrogiannis, N. Bourbaki, A survey of skin-color modeling and detection methods, Pattern Recognition, 40(3) (2007), 1106-1122.
-
[8]
R. Shakerian, S. H. Kamali, M. Hedayati, M. Alipour, Comparative Study of Ant Colony Optimization and Particle Swarm Optimization for Grid Scheduling, Journal of mathematics and computer Science (JMCS) , 3 (2011), 469-474.
-
[9]
Rouhollah Maghsoudi, Arash Ghorbannia Delavar, Somayye Hoseyny, Rahmatollah Asgari, Yaghub Heidari, Representing the New Model for Improving K-Means Clustering Algorithm based on Genetic Algorithm, Journal of mathematics and computer Science (JMCS), 2 (2011), 329 – 336.
-
[10]
, http://www.facedetection.com. , , (),
-
[11]
, http://www.imm.dtu.dk., , (),
-
[12]
, http://www.vision.caltech.edu., , (),