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Facial expressions vary from person to person, and the brightness, contrast,and resolution of every random image are different. This is why recognizingfacial expressions is very difficult. This article proposes an efficient systemfor facial emotion recognition for the seven basic human emotions (angry,disgust, fear, happy, sad, surprise, and neutral), using a convolution neuralnetwork (CNN), which predicts and assigns probabilities to each emotion. Sincedeep learning models learn from data, thus, our proposed system processes eachimage with various pre-processing steps for better prediction. Every image wasfirst passed through the face detection algorithm to include in the trainingdataset. As CNN requires a large amount of data, we duplicated our data usingvarious filters on each image. Pre-processed images of size 80*100 are passedas input to the first layer of CNN. Three convolutional layers were used,followed by a pooling layer and three dense layers. The dropout rate for thedense layer was 20%. The model was trained by combining two publicly availabledatasets, JAFFE and KDEF. 90% of the data was used for training, while 10% wasused for testing. We achieved maximum accuracy of 78.1 % using the combineddataset. Moreover, we designed an application of the proposed system with agraphical user interface that classifies emotions in real-time.

Facial Emotions Recognition using Convolutional Neural Net