Scene recognition using genetic algorithms with semantic nets
Pattern Recognition Letters
Detection and localization of faces on digital images
Pattern Recognition Letters
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
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A new method to automatically identify a human face onto a 2D gray level image is presented. The method uses an invariant description of the face and a genetic algorithm to accomplish the task. The features used are the first four translation, rotation and scale moment invariants proposed by Hu [1]. In a first step, an image possibly containing a face is first divided into small cells of fixed size of 5 × 5 pixels. For each cell, the ordinary moments are next computed. From these, the corresponding Hu's invariants are then derived. Human face identification is thus accomplished by grouping individual cells using a genetic algorithm by fitting a specific cost function. This cost function corresponds to the invariant description of a human face given in terms of the detected image features.