Kernelized Dense Layers For Facial Expression Recognition

• Published in: 2020 IEEE International Conference on Image Processing (ICIP) pp. 2226 - 2230
• Main Authors: Mahmoudi, M.Amine, Chetouani, Aladine, Boufera, Fatma, Tabia, Hedi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2020
• Subjects: Computational modeling; Convolution; facial expression recognition; fully connected layers; Kernel; kernel functions; Neurons; Task analysis; Training
• ISSN: 2381-8549
• DOI: 10.1109/ICIP40778.2020.9190694

Fully connected layer is an essential component of Convolutional Neural Networks (CNNs), which demonstrates its efficiency in computer vision tasks. The CNN process usually starts with convolution and pooling layers that first break down the input images into features, and then analyze them independently. The result of this process feeds into a fully connected neural network structure which drives the final classification decision. In this paper, we propose a Kernelized Dense Layer (KDL) which captures higher order feature interactions instead of conventional linear relations. We apply this method to Facial Expression Recognition (FER) and evaluate its performance on RAF, FER2013 and ExpW datasets. The experimental results demonstrate the benefits of such layer and show that our model achieves competitive results with respect to the state-of-the-art approaches.