Supervised LLE in ICA Space for Facial Expression Recognition

Zhao, Qijun; Zhang, D.; Lu, Hongtao

doi:10.1109/icnnb.2005.1615010

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…Unsupervised [22], [87], [110] or supervised [133], [187] non-linear feature selection techniques are less popular than linear techniques. Shan et al [134] showed that supervised techniques are usually more useful than unsupervised techniques.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Sarıyanidi

Güneş

Cavallaro

2015

IEEE Trans. Pattern Anal. Mach. Intell.

592

372

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Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions. We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition. We discuss the role of these components and highlight the models and new trends that are followed in their design. Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations; we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias. This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems.

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Section: Feature Extractionmentioning

confidence: 99%

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Sarıyanidi

Güneş

Cavallaro

2015

IEEE Trans. Pattern Anal. Mach. Intell.

592

372

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“…Both methods are closely related to previous supervised manifold learning techniques developed for the classification scenario. Specifically, Option 1.1 is akin to techniques that limit the neighborhood to points of the same class [19,11,30,20]. Interestingly, Teoh et al call this approach "neighborhood discriminant criterion" and argue that it is equivalent to the Fisher discriminant criterion [20].…”

Section: Supervised Stagementioning

confidence: 99%

“…Previous research in this area has, for the most part, viewed the problem as a classification problem wherein the viewing sphere is (artificially) quantized into non-overlapping subintervals, and head pose is represented by a set of discrete pose labels-rather than a continuum of pose angles. This approach appears to be adequate for coarse pose estimation (with some reservations) [9,10,11,12,13,14,15], and other classification problems such as facial expression and face recognition [16,17,18,19,20]. It is, however, fundamentally flawed when used for fine-grain pose estimation for two main reasons: (i) pose estimation discontinuities occur at class boundaries due to the arbitrary nature of the pose classes, (ii) the numerical properties (scale, well-ordering) of the underlying pose angles are lost; for example, the difference between pose label 1 and pose label 2 is viewed no differently than between pose labels 1 and 5.…”

Section: Introductionmentioning

confidence: 99%

Robust Head Pose Estimation Using Supervised Manifold Learning

BenAbdelkader

2010

Lecture Notes in Computer Science

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We address the problem of fine-grain head pose angle estimation from a single 2D face image as a continuous regression problem. Currently the state of the art, and a promising line of research, on head pose estimation seems to be that of nonlinear manifold embedding techniques, which learn an "optimal" low-dimensional manifold that models the nonlinear and continuous variation of face appearance with pose angle. Furthermore, supervised manifold learning techniques attempt to achieve this robustly in the presence of latent variables in the training set (especially identity, illumination, and facial expression), by incorporating head pose angle information accompanying the training samples. Most of these techniques are designed with the classification scenario in mind, however, and are not directly applicable to the regression scenario where continuous numeric values (pose angles), rather than class labels (discrete poses), are available. In this paper, we propose to deal with the regression case in a principled way. We present a taxonomy of methods for incorporating continuous pose angle information into one or more stages of the manifold learning process, and discuss its implementation for Neighborhood Preserving Embedding (NPE) and Locality Preserving Projection (LPP). Experiments are carried out on a face dataset containing significant identity and illumination variations, and the results show that our regression-based approach far outperforms previous supervised manifold learning methods for head pose estimation.

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“…Existing molecular docking schemes, such as AutoDock Vina [3], Uni-Dock [4], and LeDock [5], typically rely on conformational sampling algorithms and empirical scoring functions to search for protein and ligand binding poses and predict ligand conformations at the target protein binding site based on factors such as ligand internal energy and protein-ligand interaction energy [6]. However, these methods struggle to accurately describe various interaction forms between proteins and ligands, mainly due to simplified scoring functions for ensuring computational speed.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Application of Molecular Docking and Machine Learning for Improved Protein-Ligand Binding Pose Prediction

Yang,

Lin,

Yuan

et al. 2023

Preprint

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Accurate prediction of protein-ligand complex structures is a crucial step in structure-based drug design. Traditional molecular docking methods exhibit limitations in terms of accuracy and sampling space, while relying on machine-learning approaches may lead to invalid conformations. In this study, we propose a novel strategy that combines molecular docking and machine learning methods. Firstly, the protein-ligand binding poses are predicted using the Uni-Mol Docking machine learning approach. Subsequently, position-restricted docking(PR Docking) on predicted binding poses is performed using Uni-Dock, generating physically constrained and valid binding poses. Finally, the binding poses are re-scored and ranked using machine learning scoring functions. This strategy harnesses the predictive power of machine learning and the physical constraints advantage of molecular docking. Evaluation experiments on multiple datasets demonstrate that, compared to using molecular docking or machine learning methods alone, our proposed strategy can significantly improve the success rate and accuracy of protein-ligand complex structure predictions. This strategy is avaliable at https://github.com/dptech-corp/Uni-Dock.

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Supervised LLE in ICA Space for Facial Expression Recognition

Cited by 11 publications

References 10 publications

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Robust Head Pose Estimation Using Supervised Manifold Learning

Synergistic Application of Molecular Docking and Machine Learning for Improved Protein-Ligand Binding Pose Prediction

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