제어로봇시스템학회:학술대회논문집

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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An Improved Genetic Algorithm for Fast Face Detection Using Neural Network as Classifier

  • Sugisaka, Masanori (Department of Electrical and Electronic Engineering, Oita University, The Institute of Physical and Chemical Research (RIKEN) at Nagoya) ;
  • Fan, Xinjian (Department of Electrical and Electronic Engineering, Oita University)
  • Published : 2005.06.02
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Abstract

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.

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