FACIAL EMOTION RECOGNITION AND DETECTION IN PYTHON USING DEEP LEARNING
Facial Emotion Recognition – Humans often have different moods and facial expressions changes accordingly. Human emotion recognition plays a very important role in social relations. The automatic recognition of emotions has been an active analysis topic from early eras. In this deep learning system user’s emotions using its facial expression will be detected.
Real-time detection of the face and interpreting different facial expressions like happy, sad, angry, afraid, surprise, disgust, and neutral. etc. This system can detect six different human emotions. The trained model is capable to detect all the mentioned emotions in real-time. An automatic facial expression Recognition system has to perform detection and site of faces during a cluttered scene, facial feature extraction, and facial expression classification.
Therefore, we propose a framework of three models: CNN (deep learning algorithm), SVM (machine learning algorithm), and a novel ensemble learning algorithm and PCA as our dimensionality reduction function. The performance of our models was evaluated on JAFFE, CK+ and KDEF datasets. It was observed that our proposed models outperformed the state-of-the-art methods, with mention of our novel ensemble learning model which attained an accuracy of 100% on the JAFFE dataset.Thus we conclude that the proposed methods are effective for the recognition of anger using facial expressions and future work will look at evaluating the performance of these algorithms on a created database of Africans as well as employing these algorithms to detect anger in a persuasive space and persuade the individual from angry to another emotion for example happy.