IIITM Face Emotion

 With the expansion of machine learning and deep learning technology, facial expression recognition methods have become more accurate and precise. However, in a real case scenario, the presence of facial attributes, weakly posed expressions and variation in viewpoint can significantly reduce the performance of those systems designed only for frontal faces without facial attributes. A facial landmark distance-based model is proposed in this paper to explore a new method that can effectively recognize emotions in oriented faces with facial attributes. The proposed model computes distance-based features utilizing the inter-spaces between facial landmarks generated by a face mesh algorithm from pre-processed images. These features are normalized and ranked to find the optimal features for classifying emotions. The experimental results exhibit that the proposed model can effectively classify different emotions in the IIITM Face dataset with an overall accuracy of 61% using the SVM classifier (vertically oriented with different facial attributes). The model also classifies emotions posed in front, up, and down orientations with 70%, 58%, and 55% accuracy, respectively. The efficacy test of the model on laterally oriented faces from the KDEF database results in an overall accuracy of 80%. Comparison with the existing CNN and facial landmark-based method reveals that the proposed model exhibits an improved recognition rate for the oriented viewpoints with facial attributes. Considering the results of the proposed model, it is apparent that vertical viewpoints, forcing level of expression, and facial attributes can limit the performance of emotion recognition algorithm in realistic situations.