Kernelized dense layers for facial expression recognition

• URL: http://arxiv.org/abs/2009.10814v1
• Date: Tue, 22 Sep 2020 21:02:00 GMT
• Title: Kernelized dense layers for facial expression recognition
• Authors: M.Amine Mahmoudi, Aladine Chetouani, Fatma Boufera and Hedi Tabia
• Abstract summary: We propose a Kernelized Dense Layer (KDL) which captures higher order feature interactions instead of conventional linear relations. We show that our model achieves competitive results with respect to the state-of-the-art approaches.
• Score: 10.98068123467568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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