Hybrid CNN-RF Algorithm for Facial Expression Recognition using Different Datasets
Keywords:
Hybrid CNN-RF, FER, MTCNN, Lucy-Richardson, Contrast stretchingAbstract
Facial emotions have gained a significant importance in the area of computer vision as one can naturally express feeling non-verbally using facial expressions. A human can express a many emotion, like angry, disgust, happy, afraid, sad, neutral and surprised each emotion has a set of components. Using various deep learning techniques, many researchers have been researching in this area for facial recognition. According to existing researches, emotions may vary for trained datasets and also change in image characteristics due to high shutter speed or real time video may occur. These changes may lead to incorrect result in Facial emotion recognition. To overcome this issue, new approach of a hybrid model is proposed for Facial Emotion Recognition (FER). A hybrid model using CNN and RF Classifier with MTCNN is designed to get better performance. The collection of data is the first step in recognising and classifying face expressions in this model. These data are pre-processed for removing the unnecessary data from the raw data and are extracted for features with the MTCNN (Multi-task Cascaded Convolutional Networks) by segmenting and localizing the image. CNN-RF hybrid architecture takes extracted features as input, where CNN architecture is used in the process of feature extraction and RF is used as a classifier. The performance metrics of the proposed architecture varies for different datasets but the overall percentage for accuracy, sensitivity, precision and error of FER are 88.5%, 88%, 82%, 11.5%. Thus, the designed model instantly recognizes the facial expression and classifies the gender in effective manner.
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