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Conference Papers Year : 2020

Kernelized Dense Layers For Facial Expression Recognition

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.

Dates and versions

hal-03104971 , version 1 (10-01-2021)

Identifiers

Cite

M.Amine Mahmoudi, Aladine Chetouani, Fatma Boufera, Hedi Tabia. Kernelized Dense Layers For Facial Expression Recognition. 27th IEEE International Conference on Image Processing (ICIP 2020), Oct 2020, Abu Dhabi, United Arab Emirates. pp.2226--2230, ⟨10.1109/ICIP40778.2020.9190694⟩. ⟨hal-03104971⟩
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