Support Vector Machines in face recognition with occlusions

Jia, Hongjun; Martı́nez, Aleix M.

doi:10.1109/cvpr.2009.5206862

Cited by 89 publications

(50 citation statements)

References 11 publications

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“…al. [58] in Table 2. The (*) symbol indicates that the same subject's duplicate picture in the second session comprises of the training/test dataset.…”

Section: Facial and Expression Recognition Methodsmentioning

confidence: 99%

A Survey of the Trends in Facial and Expression Recognition Databases and Methods

Roychowdhury¹,

Emmons²

2015

IJCSES

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“…al. [58] in Table 2. The (*) symbol indicates that the same subject's duplicate picture in the second session comprises of the training/test dataset.…”

Section: Facial and Expression Recognition Methodsmentioning

confidence: 99%

A Survey of the Trends in Facial and Expression Recognition Databases and Methods

Roychowdhury¹,

Emmons²

2015

IJCSES

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“…Handling occlusions is an ongoing thread in facial recognition and some recent developments suggest the modeling of partial occlusions directly in the training sets [26]. Data-driven approaches with occluded training samples have also been explored for 2D facial feature tracking such as the AAM framework of Gross et al [24].…”

Section: Related Workmentioning

confidence: 99%

Unconstrained realtime facial performance capture

Hsieh

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

104

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We introduce a realtime facial tracking system specifically designed for performance capture in unconstrained settings using a consumer-level RGB-D sensor. Our framework provides uninterrupted 3D facial tracking, even in the presence of extreme occlusions such as those caused by hair, hand-to-face gestures, and wearable accessories. Anyone's face can be instantly tracked and the users can be switched without an extra calibration step. During tracking, we explicitly segment face regions from any occluding parts by detecting outliers in the shape and appearance input using an exponentially smoothed and user-adaptive tracking model as prior. Our face segmentation combines depth and RGB input data and is also robust against illumination changes. To enable continuous and reliable facial feature tracking in the color channels, we synthesize plausible face textures in the occluded regions. Our tracking model is personalized on-the-fly by progressively refining the user's identity, expressions, and texture with reliable samples and temporal filtering. We demonstrate robust and high-fidelity facial tracking on a wide range of subjects with highly incomplete and largely occluded data. Our system works in everyday environments and is fully unobtrusive to the user, impacting consumer AR applications and surveillance.

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“…The last step consists in classifying the extracted features. There are plenty of methods, simple ones based on distances between features via classification algorithms such as the Nearest Neighbor [15], others based on learning methods such as support vector machine [16] or neural networks [17]. However, these last methods have a significant drawback: they learn to recognize a fix number of identities, that is, classes.…”

Section: Classificationmentioning

confidence: 99%

Visible and Infrared Face Identification via Sparse Representation

Buyssens

Revenu

2013

ISRN Machine Vision

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We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches extracted from a face are decomposed in a sparse manner onto this dictionary. We particularly focus on the design of dictionaries that play a crucial role in the final identification rates. Applied to various databases and modalities, we show that this approach gives interesting performances. We propose also a score fusion framework that allows quantifying the saliency classifiers outputs and merging them according to these saliencies.

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Support Vector Machines in face recognition with occlusions

Abstract: Support Vector Machines (SVM)

Cited by 89 publications

References 11 publications

A Survey of the Trends in Facial and Expression Recognition Databases and Methods

A Survey of the Trends in Facial and Expression Recognition Databases and Methods

Unconstrained realtime facial performance capture

Visible and Infrared Face Identification via Sparse Representation

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