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PRATIT: a CNN-based emotion recognition system using histogram equalization and data augmentation
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DOI：10.1007/s11042-019-08397-0.png)
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Abstract
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Emotions are spontaneous feelings that are accompanied by fluctuations in facial muscles, which leads to facial expressions. Categorization of these facial expressions as one of the seven basic emotions - happy, sad, anger, disgust, fear, surprise, and neutral is the intention behind Emotion Recognition. This is a difficult problem because of the complexity of human expressions, but is gaining immense popularity due to its vast number of applications such as predicting behavior. Using deeper architectures has enabled researchers to achieve state-of-the-art performance in emotion recognition. Motivated from the aforementioned discussion, in this paper, we propose a model named as PRATIT, used for facial expression recognition that uses specific image preprocessing steps and a Convolutional Neural Network (CNN) model. In PRATIT, preprocessing techniques such as grayscaling, cropping, resizing, and histogram equalization have been used to handle variations in the images. CNNs accomplish better accuracy with larger datasets, but there are no freely accessible datasets with adequate information for emotion recognition with deep architectures. Therefore, to handle the aforementioned issue, we have applied data augmentation in PRATIT, which helps in further fine tuning the model for performance improvement. The paper presents the effects of histogram equalization and data augmentation on the performance of the model. PRATIT with the usage of histogram equalization during image preprocessing and data augmentation surpasses the state-of-the-art results and achieves a testing accuracy of 78.52%.
Keywords:
Emotion recognition
Convolutional neural network (CNN)
Data augmentation
Histogram equalization
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