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Intelligent web inference model based facial micro emotion detection for media store playback using artificial feed forward neural network and facial coordinate matrix

Ashok Krishna EM, Dinakaran K

The problem of facial emotion detection has been approached by various methods in several articles but suffers from the problem of poor accuracy in detecting the facial emotion. The psychological condition of any human can be obtained by facial emotions or facial reactions, which helps to solve various problems. To solve the issue of facial emotion detection, in this paper an intelligent fuzzification model is described which uses the artificial feed forward neural network and facial coordinate matrix. More than our previous methods, the proposed method considers the facial coordination matrix which stores the coordinate point of different facial components extracted at different facial emotions considered. The method extracts the facial features and computes various measures of facial features like eye size, lip sizes, nose size, and chin size. With the above-mentioned features, the method extracts the coordinate points of skin at different emotional situations. The extracted features are used to construct the neural network and the feed forward model computes various measures by each neuron and forwards them to the next layer. At each layer, the neuron computes multi-variant emotion support for each facial emotion and forwards them to the next layer. Finally, a single emotion is selected according to the computed measure based on fuzzy approach. Identified emotion is used to play songs and videos from the media store available on the server. The proposed fuzzification based method improves the performance of facial emotion detection in micro level with more accuracy.