基于小数据集下BN建模的面部表情识别

针对面部表情识别过程中获得的特征样本稀少的问题,提出了一种基于小数据集下贝叶斯网络(BN)建模的面部表情识别方法。首先提取面部表情图像的几何特征和HOG特征,经特征融合和归一化等处理构成动作单元(AU)标签样本集,其次提出了用于面部表情识别的BN结构,并将定性专家经验转化为BN条件概率之间的约束集合,随后引入凸优化最大化求解完成BN模型参数的估算,最后利用联合树推理算法识别出面部表情。实验结果表明：在小数据集条件下,与支持向量机(SVM)、Adaboost和卷积神经网络(CNN)等人脸表情分类方法相比,该方法能够取得更准确的面部表情识别结果。

Facial Expression Recognition with Small Data Sets Based by BN Modeling

Aiming at the problems of facial expression recognition with the scarce feature samples, a Bayesian network(BN) modeling facial expression recognition method based on small data sets is proposed. Firstly, the geometric feature and HOG feature of facial expression image are extracted. The feature fusion and normalization are used to form the set of action unit label samples. Secondly, the BN structure is constructed according to the expert experience. And the qualitative expert experience is transformed into the constraint set of the BN conditional probability, and the BN model parameter is derived in the light of the convex optimization. Finally, we use junction tree inference algorithm to recognize facial expressions. The experimental results show that the method can obtain more accurate facial expression recognition results than support vector machine, Adaboost or convolution neural network method under the condition of small data sets.