Emotion recognition using facial expression by fusing key points descriptor and texture features

Emotions have a great significance in human-to-human and in human-to-computer communication and interaction. In this paper, an effective and novel approach to recognize the emotions using facial expressions by the fusion of duplex features is proposed. The proposed approach broadly have three phases, phase-I: ROIs extraction, phase-2:Fusion of duplex features and phase-III: Classification. The proposed approach also gives a novel eye center detection algorithm to detect centres of the eyes. The outcome of the algorithm is further contribute to locate and partition the facial components. The hybrid combination of duplex features also gives the importance of fusion of features over individual features. The proposed approach classify the 5 basic emotions i.e. angry, happy, sad, disgust, surprise. The proposed method also raise the issue of high misclassification rate of emotions in higher age groups (>40) and successfully overcomes it. The proposed approach and its outcome evaluation is validated by using four datasets: the dataset created by us including 2500 images of 5 basic emotions (angry, happy, sad, disgust, surprise) having 500 images per emotions, CK+ dataset, MMI dataset and JAFEE dataset. Experimental results shows that the proposed work significantly improves the recognition rate (approx. 97%, 88%, 86%, 93%) and reduces the misclassification rate (approx.1.4%, 7.6%, 6.6%, 2.7%) even for the subjects of higher age group.