Emotion Recognition of Partial Face Using Star-Like Particle Polygon Estimation

The effectiveness of the existing approaches of facial expression recognition is significantly impacted by the necessity of wearing a facial mask during the Covid-19 outbreak. A large number of significant features on the lower face such as mouth, nose, and cheek, are completely eliminated. In this study, we propose an approach to extract features for recognizing emotions in a partial facial image that is based on Star-Like Particle Polygon Estimation (SLPPE). There are 3 primary steps in the proposed SLPPE. First, the lower part of the original facial input image is obscured by a synthetic mask. The process of representing regions is then used in the upper face image. Secondly, SLPPE is adopted as a feature extraction procedure. It is used to extract probability-based vectors of features that efficiently manifest the occluded facial image’s properties. Unlike other CNN based methods that bring the input image known as a raster form into several Conv modules in order to perform feature extraction process, we extract features from the original input image using our proposed SLPPE feature extraction technique in order to eliminate the issues of extracting unexpected features caused by the facial mask region provided by the Conv modules. Our proposed SLPPE feature extraction module produce the potential transformed data in the form of feature vectors without applying Conv modules. Finally, by utilizing LSTM and ANN networks, the SLPPE is adapted to the datasets of CK+, FER2013, and RAF-DB in order to evaluate the proposed methodology. With the accuracy of 99.01%, 98.7%, and 94.62% on CK+, FER2013, and RAF-DB, respectively, our proposed method outperforms the common CNN approaches which are currently in use and yields better results.