渐近真值的标签分布表情识别算法

摘要：

歧义表情是表情识别中的常见现象,由于歧义表情的存在,表情识别的效果严重受限,主要的原因是数据的单标签无法有效的描述其中复杂的感情倾向,因而标签分布学习是解决该问题的一个可能的方向。针对表情分类中单标签信息量不足的问题,提出了一种渐近真值的标签分布表情识别算法。在不引入额外信息的前提下,充分利用数据库的原本信息完成标签分布的生成和利用。首先,在数据训练时,利用单标签学习,收集数据整体分布的均值;然后,在数据批次的粒度上,逐步逼近数据标签真值;最后,利用生成的数据标签分布重新训练整个网络模型。实验结果表明,该方法对网络模型的精度提升有明显的作用,在与先进算法的对比中也有一定的竞争力。

Abstract： Ambiguous expression is a common phenomenon in facial expression recognition (FER). Because of the existence of ambiguous expression, the effect of FER is severely limited. The reason maybe that the single label of the data cannot effectively describe complex emotional intentions which are vital in FER. Label distribution learning contains more information and is a possible way to solve this problem. To apply label distribution learning on FER, a label distribution expression recognition algorithm based on asymptotic truth value is proposed. Under the premise of not incorporating extraneous quantitative information, the original information of database is fully used to complete the generation and utilization of label distribution. Firstly, in training part, single label learning is used to collect the mean value of the overall distribution of data. Then, the true value of data label is approached gradually on the granularity of data batch. Finally, the whole network model is retrained using the generated label distribution data. Experimental results show that this method can improve the accuracy of the network model obviously, and has certain competitiveness compared with the advanced algorithms.