@article{(Open Science Index):https://publications.waset.org/pdf/5603,
	  title     = {Holistic Face Recognition using Multivariate Approximation, Genetic Algorithms and AdaBoost Classifier: Preliminary Results},
	  author    = {C. Villegas-Quezada and  J. Climent},
	  country	= {},
	  institution	= {},
	  abstract     = {Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {8},
	  year      = {2008},
	  pages     = {2671 - 2675},
	  ee        = {https://publications.waset.org/pdf/5603},
	  url   	= {https://publications.waset.org/vol/20},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 20, 2008},
	}