Facial Expressions Detection Using Faster R-CNN Model
Keywords:
Facial Expressions Recognition, Faster R-CNN, Principal Component Analysis (PCA), AnnotationAbstract
In this paper, we present a method to improve facial expressions detection using a convolutional neural network (Faster R-CNN) to develop advanced automated systems in robotics and artificial intelligence applications. We focus on enhancing Fast R-CNN's performance by utilizing the FER-2013 dataset and optimizing the annotation process to crop faces and identify crucial areas within the images. This approach aims to reduce computation costs and training time. Additionally, we integrate principal component analysis (PCA) into the Faster R-CNN architecture to extract features and reduce dimensionality in input images. The proposed method involves three levels within the Faster R-CNN framework: feature extraction with PCA support in the first, a regional proposal in the second, and detection in the final. The experimental results demonstrate that our approach achieves higher accuracy and faster recognition than the same method without annotation and PCA support using the same dataset.
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