Manifold Smoothed Class-specific Discriminant Collaborative Representation for Face Recognition

Lou, Songjiang; Ma, Yong; Zhou, Wujie

doi:10.1109/iscid.2019.10124

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…Examples include SHOT [ 20 ], HoG descriptor [ 21 ], and curvature mean maps [ 22 ]. Furthermore, facial recognition has seen the implementation of similar methods [ 23 , 24 ]; an overall projection matrix was used to assemble sub-patterns, although this approach implicitly assumed the elimination of sampling or environmental noise. A brief summary of the above-mentioned works is provided in Table 1 , showing the modality and representation methods and classifying the different models.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition with Geometric Scattering on 3D Point Clouds

Cheng

et al. 2022

Sensors

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As one of the pioneering data representations, the point cloud has shown its straightforward capacity to depict fine geometry in many applications, including computer graphics, molecular structurology, modern sensing signal processing, and more. However, unlike computer graphs obtained with auxiliary regularization techniques or from syntheses, raw sensor/scanner (metric) data often contain natural random noise caused by multiple extrinsic factors, especially in the case of high-speed imaging scenarios. On the other hand, grid-like imaging techniques (e.g., RGB images or video frames) tend to entangle interesting aspects with environmental variations such as pose/illuminations with Euclidean sampling/processing pipelines. As one such typical problem, 3D Facial Expression Recognition (3D FER) has been developed into a new stage, with remaining difficulties involving the implementation of efficient feature abstraction methods for high dimensional observations and of stabilizing methods to obtain adequate robustness in cases of random exterior variations. In this paper, a localized and smoothed overlapping kernel is proposed to extract discriminative inherent geometric features. By association between the induced deformation stability and certain types of exterior perturbations through manifold scattering transform, we provide a novel framework that directly consumes point cloud coordinates for FER while requiring no predefined meshes or other features/signals. As a result, our compact framework achieves 78.33% accuracy on the Bosphorus dataset for expression recognition challenge and 77.55% on 3D-BUFE.

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Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition with Geometric Scattering on 3D Point Clouds

Cheng

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Manifold Smoothed Class-specific Discriminant Collaborative Representation for Face Recognition

Cited by 1 publication

References 33 publications

Facial Expression Recognition with Geometric Scattering on 3D Point Clouds

Facial Expression Recognition with Geometric Scattering on 3D Point Clouds

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