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In this study, an unsupervised feature selection method is proposed for facial feature recognition (FER) in the absence of class labels. The contribution is the descriptive feature components selector spectral regression representative coefficient scores based on graph manifold learning from high-dimensional feature space. The spectral regression analysis and L1-regularised least square are then used...
In facial expression recognition, high dimensional feature processing is still a hot topic since the solution to this problem can considerably reduce the time consuming operation and computational memory. Many methods have been developed to reduce feature dimension and extract the fundamental information in the feature space by projecting the original data into some lower dimensional space. In this...
To recognize expressions conveniently and effectively, an enhanced feature representation method is proposed for facial expression recognition. Local binary pattern histogram Fourier (HF-LBP) features is used to represent facial expression features. Multiple HF-LBP features are extracted to form recognition vectors for facial expression recognition in the approach, which include sign and magnitude...
An automatic facial expression recognition method is proposed to effectively recognize facial expression without any region unrelated to facial region. Support Vector Machine (SVM) is applied to recognize facial expression by Gabor features extracting using Gabor wavelet transformation after separate facial region from images Based on Active Appearance Models (AAMs), which reduce influence of illumination...
A real-time facial expressions recognition system is developed for human-robot interaction of service robot. The proposed system is mainly composed of two subsystems: one for Active shape model(ASM) motion extraction, and one for the classification of the estimated motion. The system first uses a cascade classifier to locate the potential face regions from video frame. Then, ASM is automatically initialized...
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