Kernelized dense layers for facial expression recognition

TL;DR

This paper introduces a Kernelized Dense Layer (KDL) that captures higher order feature interactions in CNNs, applied to facial expression recognition, resulting in competitive performance on standard datasets.

Contribution

The paper proposes a novel Kernelized Dense Layer for CNNs that models higher order feature interactions, enhancing facial expression recognition performance.

Findings

Achieves competitive results on RAF, FER2013, and ExpW datasets.

Demonstrates the effectiveness of KDL in capturing complex feature interactions.

Shows improved or comparable accuracy to state-of-the-art methods.

Abstract

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.