基于Boost改进的嵌入式隐马尔可夫模型的实时表情识别

Real-time Facial Expression Recognition Based on Boosted Embedded Hidden Markov Model

在人机交互过程中,理解人类的情绪是计算机和人进行交流必备的技能之一。最能表达人类情绪的就是面部表情。设计任何现实情景中的人机界面,面部表情识别是必不可少的。在本文中,我们提出了交互式计算环境中的一种新的实时面部表情识别框架。文章对这个领域的研究主要有两大贡献:第一,提出了一种新的网络结构和基于AdaBoost的嵌入式HMM的参数学习算法。第二,将这种优化的嵌入式HMM用于实时面部表情识别。本文中,嵌入式HMM把二维离散余弦变形后的系数作为观测向量,这和以前利用像素深度来构建观测向量的嵌入式HMM方法不同。因为算法同时修正了嵌入式HMM的网络结构和参数,大大提高了分类的精确度。该系统减少了训练和识别系统的复杂程度,提供了更加灵活的框架,且能应用于实时人机交互应用软件中。实验结果显示该方法是一种高效的面部表情识别方法。

Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. Facial expression recognition is necessary for designing any realistic human-machine interfaces. In this paper, we propose a novel framework to real-time facial expression recognition in the interactive computer environment. There are two main contributions of this work. First, we propose a novel network structure and parameters learning algorithm for embedded HMM based on AdaBoost. Second, we apply this optimized embedded HMM to real-time facial expression recognition. In this paper, the embedded HMM uses two-dimensional Discrete Cosine Transform （2D-DCT）coefficients as the observation vectors opposite to previous HMM approaches which use pixel intnsities to form the observation vectors. The classification accuracy is improved because our algorithm modifies both the network structure and parameters of embedded HMM. Our proposed system reduces the complexity of the training and recognition system. It can offer a more flexible framework and can be used in real-time human-machine interactive applications. Experimental results demonstrate that the proposed approach is an effective method to recognize facial expression.