Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images
This work addresses incremental improvements in facial emotion recognition for applications like human-computer interaction or psychology research.
The paper tackled the problem of improving facial emotion recognition (FER) systems by incorporating gradient and Laplacian images alongside raw input into a convolutional neural network, resulting in a 3 to 5% performance enhancement on KDEF and FERplus datasets.
In this work, we have proposed several enhancements to improve the performance of any facial emotion recognition (FER) system. We believe that the changes in the positions of the fiducial points and the intensities capture the crucial information regarding the emotion of a face image. We propose the use of the gradient and the Laplacian of the input image together with the original input into a convolutional neural network (CNN). These modifications help the network learn additional information from the gradient and Laplacian of the images. However, the plain CNN is not able to extract this information from the raw images. We have performed a number of experiments on two well known datasets KDEF and FERplus. Our approach enhances the already high performance of state-of-the-art FER systems by 3 to 5%.