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This paper presents a novel deep learning framework for facial expression recognition (FER). Our framework is derived from Convolutional Neural Networks (CNNs), adopting outline, texture, and angle raw data to generate 3 different convolutional feature maps for deep learning. In so doing, the proposed method is cable of robustly classifying expressions, by emphasizing the facial deformation, muscle...
We propose a novel and general framework, named the multithreading cascade of rotation-invariant histograms of oriented gradients (McRiHOG) for facial expression recognition (FER). In this paper, we attempt to solve two problems about high-quality local feature descriptors and robust classifying algorithm for FER. The first solution is that we adopt annular spatial bins type HOG (Histograms of Oriented...
This paper proposes a novel machine-learning framework for facial-expression recognition, which is capable of processing images fast and accurately even without having to rely on a large-scale dataset. The framework is derived from Support Vector Machines (SVMs) but distinguishes itself in three key ways. First, the measure of the samples normalization is based on the Perturbed Subspace Method (PSM),...
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