Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/16240
Title: Learning to recognize gender using experince
Authors: Castrillón-Santana, Modesto 
Lorenzo Navarro, José Javier 
Freire-Obregón, David 
Déniz Suárez, Oscar
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Facial analysis
Online learning
Gender
Issue Date: 2010
Project: Tecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos. 
Journal: Proceedings - International Conference on Image Processing 
Abstract: Automatic facial analysis abilities are commonly integratedin a system by a previous off-line learning stage. In this pa-per we argue that a facial analysis system would improve itsfacial analysis capabilities based on its own experience simi-larly to the way a biological system, i.e. the human system,does throughout the years. The approach described, focusedon gender classification, updates its knowledge according tothe classification results. The presented gender experiments suggest that this approach is promising, even when just a short simulation of what for humans would take years of acquisition experience was performed.
ISBN: 978-1-4244-7994-8
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5653661
Source: Proceedings of 2010 IEEE 17th in Conference on Image Processing, ICIP (5653661), Hong Kong, China, september 26-29, ISSN 1522-4880, p. 4533-4536
Appears in Collections:Actas de congresos
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