Monocular head pose estimation using generalized adaptive view-based appearance model

Morency, Louis–Philippe; Whitehill, Jacob; Movellan, Javier R.

doi:10.1016/j.imavis.2009.08.004

Cited by 38 publications

(24 citation statements)

References 31 publications

(44 reference statements)

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“…Flexible models include methods such as active shape models (ASM), active appearance models (AAM), and elastic graph matching (EGM). In [19], authors present a probabilistic framework which do not need user initialization unlike most of flexible models which do not respect the requirement (R2).…”

Section: Model-based Head Pose Estimationmentioning

confidence: 99%

Driver head pose estimation using efficient descriptor fusion

Alioua

Amine

Rogozan

et al. 2016

J Image Video Proc.

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A great interest is focused on driver assistance systems using the head pose as an indicator of the visual focus of attention and the mental state. In fact, the head pose estimation is a technique allowing to deduce head orientation relatively to a view of camera and could be performed by model-based or appearance-based approaches. Modelbased approaches use a face geometrical model usually obtained from facial features, whereas appearance-based techniques use the whole face image characterized by a descriptor and generally consider the pose estimation as a classification problem. Appearance-based methods are faster and more adapted to discrete pose estimation. However, their performance depends strongly on the head descriptor, which should be well chosen in order to reduce the information about identity and lighting contained in the face appearance. In this paper, we propose an appearancebased discrete head pose estimation aiming to determine the driver attention level from monocular visible spectrum images, even if the facial features are not visible. Explicitly, we first propose a novel descriptor resulting from the fusion of four most relevant orientation-based head descriptors, namely the steerable filters, the histogram of oriented gradients (HOG), the Haar features, and an adapted version of speeded up robust feature (SURF) descriptor. Second, in order to derive a compact, relevant, and consistent subset of descriptor's features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. As we show in experiments with the public database (Pointing'04) as well as with our real-world sequence, our approach describes the head with a high accuracy and provides robust estimation of the head pose, compared to state-of-the-art methods.

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Section: Model-based Head Pose Estimationmentioning

confidence: 99%

Driver head pose estimation using efficient descriptor fusion

Alioua

Amine

Rogozan

et al. 2016

J Image Video Proc.

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“…Head poses were tracked using the GAVAM head tracker (Morency, Whitehill, & Movellan, 2010) from the video data. Unreliable data (with confidence less than 7.0) were marked as missing.…”

Section: Max(log(f))/mean(log(f))mentioning

confidence: 99%

Designing An Automated Assessment of Public Speaking Skills Using Multimodal Cues

Chen¹,

Feng²,

Leong³

et al. 2016

Learning Analytics

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Traditional assessments of public speaking skills rely on human scoring. We report an initial study on the development of an automated scoring model for public speaking performances using multimodal technologies. Task design, rubric development, and human rating were conducted according to standards in educational assessment. An initial corpus of 17 speakers with 4 speaking tasks was collected using audio, video, and 3D motion capturing devices. A scoring model based on basic features in the speech content, speech delivery, and hand, body, and head movements significantly predicts human rating, suggesting the feasibility of using multimodal technologies in the assessment of public speaking skills.

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“…Recent research has investigated how to combine absolute tracking with differential tracking. The differential tracking algorithms provide speed and accuracy over short time scales, and the absolute tracking algorithms help with error recovery and rapid adaptation to faces moving in and out of the image plane [38].…”

Section: Differential Face Detectorsmentioning

confidence: 99%

“…Depending on the approach the morphing may use planar models of the face [34,29], cylindrical models of the face [27], ellipsoid models [38], 2D active appearance models based on a triangulated mesh [32], or 3D deformable models [34,5,55].…”

Section: Facial Feature Detectionmentioning

confidence: 99%