基于生物启发特征的真实环境笑脸分类方法

为解决生物启发模型(BIM)存在的3个问题，即高计算复杂度、有争议的视觉皮层关系建模，以及类前向反馈机制带来的盲目特征选择，提出一种基于生物启发特征(BIF)的真实环境笑脸分类方法。构建基于BIF的笑脸分类系统，提取人脸表情图像嘴部区域的金字塔梯度方向直方图特征，使用局部保持投影进行BIM特征降维，采用Adaboost算法进行BIM特征选择。实验结果验证，该系统的最佳识别率达96.5%。

Classification Method of Smiling Face in Real Environment Based on Biologically Inspired Feature

To deal with the following three issues： the high computational complexity,the controversial modeling on the visual cortex relationship,and the blind feature selection due to the characteristics of similar feed-forward framework that exists in Biologically Inspired Model（BIM）,this paper proposes to build a smiling face classification system based on BIM.It extracts Pyramid Histogram of Oriented Gradients（PHOG） features form the mouth region of the facial expression image,reduces the BIM feature vector dimensions using Locality Preserving Projection（LPP）,and adopts Adaboost algorithm for BIM feature selection.Experimental results demonstrate that the best recognition rate is up to 96.5%.