Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics.

Human emotion recognition from videos involves accurately interpreting facial features, including face alignment, occlusion, and shape illumination problems. Dynamic emotion recognition is more important. The situation becomes more challenging with multiple persons and the speedy movement of faces. In this work, the ensemble max rule method is proposed. For obtaining the results of the ensemble method, three primary forms, such as CNN_HOG-KLT, CNN_Haar-SVM, and CNN_PATCH are developed parallel to each other to detect the human emotions from the extracted vital frames from videos. The first method uses HoG and KLT algorithms for face detection and tracking. The second method uses Haar cascade and SVM to detect the face. Template matching is used for face detection in the third method. Convolution neural network (CNN) is used for emotion classification in CNN_HOG-KLT and CNN_Haar-SVM. To handle occluded images, a patch-based CNN is introduced for emotion recognition in CNN_PATCH. Finally, all three methods are ensembles based on the Max rule. The CNN_ENSEMBLE for emotion classification results in 92.07% recognition accuracy by considering both occluded and nonoccluded facial videos. [ABSTRACT FROM AUTHOR]