مشخصات پژوهش

عنوان	Facial Expression Recognition using Spatial Feature Extraction And Ensemble Deep Networks
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Facial Expression Recognition, Local Binary Pattern, Image Spatial Features, Ensemble Networks, Deep Learning, Artificial Intelligence, Machine Vision
چکیده	Researchers have shown that 55% of concepts are conveyed through facial emotion and only 7% are conveyed by words and sentences, so facial expression plays an important role in conveying concepts in human communications. In recent years, due to the improvement of artificial neural networks, many studies have been conducted related to facial expression recognition. This paper presents a method based on ensemble classification using convolutional neural networks to recognize facial emotions. The concatenation of spatial feature images to global features is used as a feature map for the classification stage in the committee network. Two committee networks are fed separately with LBP and raw images. After training the two committee networks, to classify the emotion, the maximum probability between the two networks is considered as the final output. The proposed method was applied and tested on the FER2013 dataset. The experimental results show that the mechanism has a good effect and our proposed model achieves quite promising performance on the dataset.
پژوهشگران	الهام افشار (نفر اول)، حسن ختن لو (نفر دوم)، الهام علی قارداش (نفر سوم)