Emotion Recognition Using Neural Network Approaches: A Review
Authors: Ms.Prerna R. Ingle1
Number of views: 578
It is very interesting to recognize the human gesture for general life applications. For example, observing the gesture of a driver when he/she is driving and alerting him/her when in sleepy mood will be quite useful. Human gestures can be identified by observing the different movements of eyes, mouth, nose and hand. There are number of techniques which we use for recognizing the facial expression. Facial expressions are generated by contractions official muscles, which results in temporally deformed facial features such as eye lids, eye brows, nose, lips and skin texture, often revealed by wrinkles and bulges. The term face recognition refers to identifying, by computational algorithms, an unknown face image. Facial expressions give us information about the emotional state of the person. Moreover, these expressions help in understanding the overall mood of the person in a better way. Facial expressions play an important role in human interactions and non-verbal communication. Classification of facial expressions could be used as an effective tool in behavioral studies and in medical rehabilitation. Facial expression analysis deals with visually recognizing and analyzing different facial motions and facial feature changes. This operation can be done by comparing the unknown face with the faces stored in database. Face recognition has three stage , face location detection, feature extraction and facial image classification.
In this research, we carry out a study to recognize basic emotions (sadness, surprise, happiness, anger, and fear ). Also, we propose a methodology and Neural Network for classification of emotions based facial features extraction.
The aim of this research is to develop an efficient identification algorithm based on computational intelligence approaches, with accuracy similar to that achieved by experienced Analyst.