Facial expressions recognition system using Bayesian inference
Maninderjit Singh, Anima Majumder, Laxmidhar Behera · 2014
The paper presents a facial expressions recognition system using Bayesian network. We train the network using probabilistic modeling that draws relationship between facial features, action units and finally recognizes six basic emotions. We propose features extraction methods to get geometric feature vector containing angular informations and appearance feature vector containing moments extracted after applying gabor filter over certain facial regions. Both the feature vectors are further used to draw relationships among Action Units (AUs). The angular informations are directly extracted from the facial landmark points. The geometric features extraction approach contains only 22 dimensional angular informations against direct facial landmarks based approach that contains 136 dimensional feature vector. Facial activities are represented by three distinct layers. Bottom level contains landmark measurement data with angular features. Middle level has facial AUs those are coded in facial action coding system (FACS) and the top level, represents emotion node. We also propose a method using k-means clustering to automatically define the states of nodes in anatomical layer that draws relationship among AUs and measurement data. Extended Cohn Kanade Database is being used for our experimental purposes. An average emotion recognition accuracy of 95.7% is achieved using proposed Bayesian network based approach for 22 dimensional angular feature vector. To verify the performance of the proposed approach we apply three different classifiers such as, Support vector machine, Decision tree and Radial basis functions network. The confusion matrices show that the Bayesian network based classification approach outperforms all other applied approaches. The experimental results illustrates the effectiveness of the proposed model.