LOCATION AND SEGMENTATION OF FACIAL FEATURES COMBINING PSO ALGORITHM AND SKIN COLOR
DOI:
https://doi.org/10.53555/eijse.v5i3.69Keywords:
Face features, edge detection, particle swarm optimization, skin segmentation, cubic splineAbstract
A new method using PSO algorithm and skin color for the location and segmentation of face and facial features is proposed. In the preprocessing stage, segmented face image is obtained from initial color image. To achieve this goal, PSO algorithm is applied to search for the best face region. Then, based on the edge density of face image, eye region is located with PSO. Then, lips region is located using color component of skin segmentation. Finally, nose region is segmented based on the result of eyes and lips. The Simulation results show that this hybrid method is accurate and effective.
References
. J. Hddadnia, K. Faez, M. Ahmadi. An Efficient Human Face Recognition System Using Pseudo Zernike Moment Invariant and Radial Basis Function Neural Network, International Journal of Pattern Recognition and Artificial Intelligence, 2003, 17(1): 41‐62.
. Hamidreza Rashidy Kanan, Mohammad Hassan Moradi. A Genetic Algorithm based Method for Face Localization and pose Estimation. Proceedings of the third International Conference: Sciences of Electronic, Technologies of Information and Telecommunications. 2005.
. J. Wang, T. Tan. A new face detection method based on shape information. Pattern Recognition Letters. 2000, 21(6‐7): 463‐471.
. K.J. Kirchberg, O. Jesorsky, W. Robert. Frischholz. Genetic Model Optimization for Hausdorff Distance‐Based Face Localization. Proceedings of the International ECCV 2002 Workshop on Biometric Authentication, 2002, pp. 103‐111.
. K. Sobotta, I. Pitas. Face localization and facial feature extraction based on shape and color information. Proceedings of the International Conference on Image Processing, 1996: 483‐486.
. R. Herpers, G. Verghese, K. Derpains et al. Detection and tracking of face in real environments. Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real‐Time Systems, 1999, pp. 96‐104.
. W. Zhao, R. Chellappa, P.J. Phillips, et al. Face recognition: A literature survey. ACM Computing Surveys, 2003, 35(4): 399‐458.
. Jin Hong‐wei. The research and Realization of face detection technology based on the color and structural features. Doctor of Engineering thesis, National University of Defense Technology. 2007: 1‐5.
. LIU Xiang‐lou，ZHANG Ming，DENG Yan‐ru. A Kind of Segmentation Method of Face Object. Science Technology and Engineering, 2011, 11(12): 2686‐2690.
. J. Kennedy, R.C. Eberhart. Particle Swarm Optimization. Proceedings IEEE International Conference on Neural Networks, 1995, pp. 1942‐1948.
. R.C. Eberhart, Y. Shi. Particle swarm optimization: developments, applications and resources. Proceedings of the IEEE Congress on Evolutionary Computation, 2001, pp. 81‐86.
. X.D. Duan, C.R Wang, X.D. Liu. Particle Swarm Optimization and Application. Liaoning University Press, Shenyang, 2005.
. W.F. Abd‐El‐Wahed, A.A. Mousa, M.A. El‐Shorbagy. Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems, Journal of Computational and Applied Mathematics, vol. 235, no. 5, pp. 1446‐1453, 2011.
. Jian Zhang, Experimental Parameter Investigations on Particle Swarm Optimization Acceleration Coefficients, International Journal of Advancements in Computing Technology, Vol. 4, No. 5, pp. 99‐105, 2012.
. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins. Digital Image Processing Using MATLAB. Publishing House of Electronics Industry, 2012. 4.
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