Facial Emotion Analysis and Recommendation Using CNN

Managing one’s emotions in the workplace is more important nowadays than it ever has been. The current approach shows fluctuation in emotion prediction as there is a need for strong correlation between the input images and fused image, fluctuating illumination environments may impact the fitting process and lessen the recognition correctness, lack in training dataset. To address this problem, this paper explores different types of algorithms, neural networks and machine learning techniques which can be used as a base to increase the efficiency as well as the robustness of our model. The proposed model consists of modules, one will load the 48X48 pixel grayscale images of faces from FER2013 datasets, pre-process it and uses a CNN classifier to classify the acquired image into different emotion categories and the other module uses a Haar-Cascade feature to detect the face and predicts the corresponding emotions and displaying an audio or video recommendation in the output. This will help to analyse the sentimental state of an individual, providing robustness against low illumination, reducing fitting process.