Simultaneous evolutionary neural network based automated video based facial expression analysis


  • S. Jeyalaksshmi
  • S. Prasanna





Human computer interaction (HCI), facial expressions and emotions, simultaneous evolutionary neural network (SENN), adaboost, geometric features, local binary pattern (LBP), classification.


In real life scenario, facial expressions and emotions are nothing but responses to the external and internal events of human being. In Human Computer Interaction (HCI), recognition of end user’s expressions and emotions from the video streaming plays very important role. In such systems it is required to track the dynamic changes in human face movements quickly in order to deliver the required response system. In real time applications, this Facial Expression Recognition (FER) is very helpful like physical fatigue detection based on facial detection and expressions such as driver fatigue detection in order to prevent the accidents on road. Face expression based physical fatigue analysis or detection is out of scope of this work, but this work proposed a Simultaneous Evolutionary Neural Network (SENN) classification scheme is proposed for recognising human emotion or expression. In this work, at first, automatically detects and tracks facial landmarks in videos, and face is detected by using enhanced adaboost algorithm with haar features. Then, in order to describe facial expression modifications, geometric features are taken out and the Local Binary Pattern (LBP) is extracted to improve the detection accuracy and it has a much lower-dimensional size. With the aim of examining the temporal facial expression modifications, we apply SENN probabilistic classifiers, which examine the facial expressions in individual frames, and after that promulgate the likelihoods during the course of the video to take the temporal features of facial expressions such as glad, sad, anger, and fear feelings. The experimental results show that the performance of proposed SENN scheme is attained better results compared than existing recognition schemes like Time-Delay Neural Network with Support Vector Regression (TDNN-SVR) and SVR. 


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