Altaf Muhammad Faheem, Iqbal Muhammad Waseem, Ali Ghulam, Shinan Khlood, Alhazmi Hanan E, Alanazi Fatmah, Ashraf M Usman
Department of Computer Science, Superior University Lahore, Lahore, Pakistan.
Department of Software Engineering, Superior University Lahore, Lahore, Pakistan.
PLoS One. 2025 Mar 19;20(3):e0316562. doi: 10.1371/journal.pone.0316562. eCollection 2025.
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.
在本文中，我们开发了一种姿态感知面部表情识别技术。所提出的技术采用K近邻算法进行姿态检测，并使用基于神经网络的扩展堆叠集成模型进行姿态感知面部表情识别。对于姿态感知面部表情分类，我们将堆叠集成技术从两级集成模型扩展到了三级集成模型：基础级、元级和预测器。基础级分类器是二元神经网络。元级分类器是一组二元神经网络。二元神经网络的输出使用概率分布进行组合，以构建神经网络集成。训练一组神经网络集成来学习多姿态面部表情之间的相似性，其中每个神经网络集成表示面部表情的存在或不存在。预测器是朴素贝叶斯分类器，它获取堆叠神经网络集成的二元输出，并将未知面部图像分类为面部表情之一。使用Viola-Jones面部检测器检测面部集中区域。拉德堡德人脸数据库用于堆叠集成的训练和测试。实验结果表明，所提出的技术使用特征脸特征和160个堆叠神经网络集成以及朴素贝叶斯分类器时，准确率达到了90%。这表明与现有技术的姿态感知面部表情识别技术相比，所提出的技术表现显著。
