Palabras clave
natación-resistencia-actividad fÃsica- desempeño-calidad de vida
Cómo citar
Resumen
Today, the topic of human identification based on physical characteristics is a necessity in various fields. As a biometric system, a facial recognition system is fundamentally a pattern recognition system that identifies a person based on specific physiological or behavioral feature vectors. The feature vector is typically stored in a database upon extraction. The main objective of this research is to study and assess the effect of selecting the proper image attributes using the Cuckoo search algorithm. Thus, the selection of an optimal subset, given the large size of the feature vector dimensions to expedite the facial recognition algorithm is essential and substantial. Initially, by using the existing database, image characteristics are extracted and selected as a binary optimal subset of facial features using the Cuckoo algorithm. This subset of optimal features are evaluated by classifying nearest neighbor and neural networks. By calculating the accuracy of this classification, it is clear that the proposed method is of higher accuracy compared to previous methods in facial recognition based on the selection of significant features by the proposed algorithm.
Citas
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, IEEE. Vol. 1, pp. I-511.
Govindarajan, V., VVS, S. K., & Ramachandran, S. (2012). Face recognition using block-based DCT feature extraction. Journal of Advanced Computer Science & Technology, 1(4), 266-283.
Han, M., & Liu, X. (2013). Feature selection techniques with class separability for multivariate time series. Neurocomputing, 110, 29-34.
Yildiz, A. R. (2013). Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. The International Journal of Advanced Manufacturing Technology, 64(1-4), 55-61.
Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with computers, 29(1), 17-35.
Charles A. N. (2001). The Development and Neural Bases of Face Recognition. John Wiley & Sons, Ltd, 239.
Nagi J., Ahmed S. K., Nagi F., (2007). A MATLAB based Face Recognition System using Image Processing and Neural Networks. 4th International Colloquium on Signal Processing and its Applications, March 7-9, 2008.
Pratibha S., Sunny B., Pritpal S., (2016). Face Recognition System Using Genetic Algorithm. International Conference on Computational Modeling and Security (CMS 2016), May, 410-417.
Deng W., Jiani H., Zhongjun W., Jun G., (2017). Lighting-aware face frontalization for unconstrained face recognition. Elsevier Ltd, 0031-3203.
Liua Q., Wanga C., Jing X. Y. (2017). Dual multi-kernel discriminant analysis for color face recognition. Optik 139, 185–201.
Donoso R., Martín C. S., Hermosilla G, (2017). Reduced isothermal feature set for long wave infrared (LWIR) face recognition. Infrared Physics & Technology 83, 114–123.
Lingraj D., Sanjay A., Rutuparna P., Ajith A., (2017). An evolutionary single Gabor kernel based filter approach to face recognition, Engineering Applications of Artificial Intelligence, 286–301.
Ding C., Tao D. (2017). Pose-invariant face recognition with homography-based normalization. Pattern Recognition 66, 144–152.
Tiwari, V. (2012). Face recognition based on cuckoo search algorithm. Image, 7(8), 9.
Hetal R. S., Rajesh C. S.., (2014). Towards the Improvement of Cuckoo Search Algorithm, International Journal of Computer Information Systems and Industrial Management Applications. ISSN 2150-7988 Volume 6, pp.77-88.
Yang, X. S. (2014). Cuckoo Search and Firefly Algorithm. Springer: Berlin/Heidelberg, Germany.
Xin-She Y., Suash D., (2009). Cuckoo Search via L ´evy Flights, IEEE, 978-1-4244-5612-3/09/.