Notations and description.

In this paper, we developed a pose-aware facial expression recognition technique. The proposed technique employed K nearest neighbor for pose detection and a neural network-based extended stacking ensemble model for pose-aware facial expression recognition. For pose-aware facial expression classification, we have extended the stacking ensemble technique from a two-level ensemble model to three-level ensemble model: base-level, meta-level and predictor. The base-level classifier is the binary neural network. The meta-level classifier is a pool of binary neural networks. The outputs of binary neural networks are combined using probability distribution to build the neural network ensemble. A pool of neural network ensembles is trained to learn the similarity between multi-pose facial expressions, where each neural network ensemble represents the presence or absence of a facial expression. The predictor is the Naive Bayes classifier, it takes the binary output of stacked neural network ensembles and classifies the unknown facial image as one of the facial expressions. The facial concentration region was detected using the Voila-Jones face detector. The Radboud faces database was used for stacked ensembles’ training and testing purpose. The experimental results demonstrate that the proposed technique achieved 90% accuracy using Eigen features with 160 stacked neural network ensembles and Naive Bayes classifier. It demonstrates that the proposed techniques performed significantly as compare to state of the art pose-ware facial expression recognition techniques.本文提出一种姿态感知（pose-aware）面部表情识别技术。所提技术采用K近邻（K nearest neighbor）算法完成姿态检测，并基于神经网络构建扩展堆叠集成模型，用于姿态感知的面部表情识别任务。针对姿态感知面部表情分类任务，我们将堆叠集成技术从两级集成模型拓展至三级集成模型，分别为基级、元级与预测级。基级分类器采用二分类神经网络；元级分类器由一组二分类神经网络构成，通过概率分布融合各二分类神经网络的输出以构建神经网络集成。我们训练了一组神经网络集成，用于学习多姿态面部表情间的相似度，其中每个神经网络集成对应某一类面部表情的存在与否。预测级为朴素贝叶斯（Naive Bayes）分类器，其接收堆叠神经网络集成的二值输出，并将未知面部图像分类至对应面部表情类别。本研究采用Voila-Jones人脸检测器提取面部感兴趣区域，并使用拉德布德人脸数据库（Radboud faces database）完成堆叠集成模型的训练与测试。实验结果表明，当采用160个堆叠神经网络集成结合朴素贝叶斯分类器，并使用特征脸（Eigen features）特征时，所提技术的识别准确率可达90%。相较于现有最优（state of the art）姿态感知面部表情识别技术，本研究所提方法的性能表现更为优异。