期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：5
页码：902-907
DOI：10.35629/5252-0305776780
语种：English
出版社：IJAEM JOURNAL
摘要：Facial emotion recognition is the process of detecting human emotions from facial expressions. The human brain recognizes emotions automatically, and software has now been developed that can recognize emotions as well. This technology is becoming more accurate all the time, and will eventually be able to read emotions as well as our brains do. AI can detect emotions by learning what each facial expression means and applying that knowledge to the new information presented to it. Emotional artificial intelligence, or emotion AI, is a technology that is capable of reading, imitating, interpreting, and responding to human facial expressions and emotions. Facial expression is an effective way for humans to communicate since it contains critical and necessary information regarding human affective states. It is a critical part of affective computing systems that aim to recognize and therefore better respond to human emotions. Automatic recognition of facial expressions can be an important component in human-machine interfaces, human emotion analysis, and decision making. As a result, facial expression recognition has become a prominent research topic in human-computer interaction, as well as in the fields of image processing, pattern recognition, machine learning, and human recognition. In this project, we will implement the techniques to automatically detect facial parts using HAAR CASCADES algorithm and classify the emotions using Long Short Term Memory algorithm,. To recognize emotion using the correlation of the facial feature sequence, a deep neural network for emotion recognition based on LSTM is proposed. The first layer of the deep neural network is the LSTM layer, which is used to mine the context correlation in the input facial feature sequence. The second layer is the fullconnect layer, which is used to integrate information and act as the major role of the classifier. And present playlist of songs which is suitable for his current mood using K-Nearest Neighbor classification algorithm. In testing side, would supply a test image whose expression it desires to recognize. This test image would be matched with facial databases to play music based on recognized emotions. Finally provide emotion based music player with improved recognition rate.
