The AudioVisual Arabic Dataset for Natural Emotions Emotions are a crucial aspect of human life and the researchers have tried to build an automatic emotion recognition system that helps to provide important realworld applications. The psychologists have shown that emotions differ across culture, considering this fact, we provide and describe the first audiovisual Arabic emotional dataset which called (AVANEmo). In this work we aim to fill the gap between studies of emotion recognition for Arabic content and other languages by provided an Arabic dataset which is a major and fundamental part of build emotion recognition application. Our dataset contains 3000 clips for video and audio data, and it covers six basic emotional labels (Happy, Sad, Angry, Surprise, Disgust, Neutral). Also, we provide some baseline experiments to measure the primitive performance for automated audio and visual emotion recognition application using the AVANEmo dataset. The best accuracy that we achieved was 54.5% and 57.9% using the audio and visual data respectively. The data will be available for distribution to researchers.

Affective Video Content Analysis Based on Two Compact AudioVisual Features In this paper, we propose a new framework for affective video content analysis by using two compact audiovisual features. In the proposed framework, the eGeMAPS is first calculated as global audio feature and then the key frames of optical flow images are fed to VGG19 network for implementing the transfer learning and visual feature extraction. Finally for model learning, the logistic regression is employed for affective video content classification. In the experiments, we perform the evaluations of audio and visual features on the dataset of Affective Impact of Movies Task 2015 (AIMT15), and compare our results with those of competition teams participated in AIMT15. The comparison results show that the proposed framework can achieve the comparable classification result with the first place of AIMT15 with a total feature dimension of 344, which is only about one thousandth of feature dimensions used in the first place of AIMT15.