关键词: 人脸识别, 遗传算法, 神经网络, 数据复杂度, 粒子选择

Abstract: Aimed at the problem that the face recognition accuracy is low under unconstrained condition, a granular neural network and genetic optimization algorithm based face recognition algorithm is proposed. Firstly, the face dataset is initially analyzed to decide the faces distribution in each granularity, and the data points with the same complexity level are grouped to a group; then, the faces are divided to front, eyes, mouth, and the sub-regions of the face are trained by granular neural network with different counts of data points, and multi training results are generated; lastly, a multi-level genetic algorithm is designed to optimize the granular neural network. Two public face databases based compared experimental results show that the proposed algorithm releases better face recognition accuracy than the other algorithms.

Key words: face recognition, genetic algorithm, neural networks, data complexity, granular selection