09-09-2017, 04:43 PM
The system consists of two different trainer and evaluator entities. Each video feed frame is passed through a series of steps including haar classifiers, skin detection, feature extraction, feature spot tracking, creation of a learned Vector Support model to classify emotions to achieve a balance between accuracy and rate of outcome. A processing time of 100-120 ms per 10 frames was achieved with an accuracy of about 60%.
The main component of human communication are the facial expressions that constitute about 55 percent of the total message communicated. We use facial expressions not only to express our emotions, but also to provide important communicative cues during social interaction, such as our level of interest, our desire to take a conversation turn, and continuous feedback signaling the understanding of the information conveyed. In recent years there has been a worldwide appetite for the recognition of facial expression. A number of methods have been proposed, but no single method has yet been found that is efficient in terms of both memory and time complexity.
In the last decade or two, there has been a significant effort in the development of facial expression recognition methods, which is an attractive research topic because of its great potential in real-life applications such as human-computer interaction (HCI), emotional computation and digital albums There are a number of difficulties due to the variation and complexity of facial expression across the human population and even the same individual. Sometimes humans even make mistakes.
The main component of human communication are the facial expressions that constitute about 55 percent of the total message communicated. We use facial expressions not only to express our emotions, but also to provide important communicative cues during social interaction, such as our level of interest, our desire to take a conversation turn, and continuous feedback signaling the understanding of the information conveyed. In recent years there has been a worldwide appetite for the recognition of facial expression. A number of methods have been proposed, but no single method has yet been found that is efficient in terms of both memory and time complexity.
In the last decade or two, there has been a significant effort in the development of facial expression recognition methods, which is an attractive research topic because of its great potential in real-life applications such as human-computer interaction (HCI), emotional computation and digital albums There are a number of difficulties due to the variation and complexity of facial expression across the human population and even the same individual. Sometimes humans even make mistakes.