A New Hybrid Approach for Efficient Emotion Recognition using Deep Learning

Facial emotion recognition has been very popular area for researchers in last few decades and it is found to be very challenging and complex task due to large intra-class changes. Existing frameworks for this type of problem depends mostly on techniques like Gabor filters, principle component analysis (PCA), and independent component analysis(ICA) followed by some classification techniques trained by given videos and images. Most of these frameworks works significantly well image database acquired in limited conditions but not perform well with the dynamic images having varying faces and images. In the past years, various researches have been introduced framework for facial emotion recognition using deep learning methods. Although they work well, but there is always some gap found in their research. In this research, we introduced hybrid approach based on RNN and CNN which are able to retrieve some important parts in the given database and able to achieve very good results on the given database like EMOTIC, FER-13 and FERG. We are also able to show that our hybrid framework is able to accomplish promising accuracies with these datasets.