Facial Emotion Recognition using DCNN Algorithm

Facial emotion recognition (FER) is critical for human-computer interaction in areas like clinical practice and behavioral description. With the heterogeneity of human faces and kinds of
images like various facial poses such as happy, angry, sad, fear, disgust, surprised etc. and lighting, accurate and robust FER by computer models remains a challenge. Deep learning
models, particularly Deep Convolutional Neural Networks (DCNNs), have shown great promise among all FER techniques due to their powerful automatic feature extraction and computational of efficiency. On the FER2013 dataset, the
highest single-network classification accuracy has been attained in this paper. The VGGNet architecture is used, its hyper parameters are fine-tuned, and different optimization techniques are performed. This proposed model has
achieved the state-of-the-art single-network accuracy of 90% on FER2013 without using any additional training data.