Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models

Sung, Jaewon; Kanade, Takeo; Kim, Daijin

doi:10.1007/s11263-007-0125-1

Cited by 75 publications

(42 citation statements)

References 16 publications

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“…Rigid 3D tracking has also been studied by using generic 3D models of the face (Malciu and Prěteux 2000;La Cascia et al 2000). For example, La Cascia et al (2000) formulate the tracking task as an image registration problem in the cylindrically unwrapped texture space and Sung et al (2008) combine active appearance models (AAMs) with a cylindrical head model for robust recovery of the global rigid motion. Currently, rigid face tracking is generally treated along the same lines as general model free object tracking (Jepson et al 2003;Smeulders et al 2014;Liwicki et al 2013Liwicki et al , 2012bRoss et al 2008;Li et al 2016a).…”

Section: Prior Artmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

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Section: Prior Artmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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“…Relevant to this paper is the approach proposed in [40]. The authors combine a CHM with an AAM approach to overcome the sensitivity to large pose variations, initial pose parameters, and problems of re-initialization.…”

Section: Synergetic Eye Location and Chm Trackingmentioning

confidence: 99%

Combining Head Pose and Eye Location Information for Gaze Estimation

Valenti

Sebe

Gevers

2012

IEEE Trans. on Image Process.

300

136

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Abstract-Head pose and eye location for gaze estimation have been separately studied in numerous works in the literature. Previous research shows that satisfactory accuracy in head pose and eye location estimation can be achieved in constrained settings. However, in the presence of nonfrontal faces, eye locators are not adequate to accurately locate the center of the eyes. On the other hand, head pose estimation techniques are able to deal with these conditions; hence, they may be suited to enhance the accuracy of eye localization. Therefore, in this paper, a hybrid scheme is proposed to combine head pose and eye location information to obtain enhanced gaze estimation. To this end, the transformation matrix obtained from the head pose is used to normalize the eye regions, and in turn, the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to enhance the accuracy of eye location estimations, particularly in low-resolution videos, to extend the operative range of the eye locators, and to improve the accuracy of the head pose tracker. These enhanced estimations are then combined to obtain a novel visual gaze estimation system, which uses both eye location and head information to refine the gaze estimates. From the experimental results, it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Furthermore, it considerably extends its operating range by more than 15 by overcoming the problems introduced by extreme head poses. Moreover, the accuracy of the head pose tracker is improved by 12% to 24%. Finally, the experimentation on the proposed combined gaze estimation system shows that it is accurate (with a mean error between 2 and 5 ) and that it can be used in cases where classic approaches would fail without imposing restraints on the position of the head.Index Terms-Eye center location, gaze estimation, head pose estimation. I. MOTIVATION AND RELATED WORK IMAGE-BASED gaze estimation is important in many applications, spanning from human-computer interaction (HCI) to human behavior analysis. In applications where human activity is under observation from a static camera, the There is an abundance of literature concerning these two topics separately; recent surveys on eye center location and head pose estimation can be found in [17] and [31]. The eye location algorithms found in commercially available eye trackers share the problem of sensitivity to head pose variations and require the user to be either equipped with a head-mounted device or to use a high-resolution camera combined with a chin rest to limit the allowed head movement. Furthermore, daylight applications are precluded due to the use of active infrared (IR) illumination to obtain accurate eye location through corneal reflection. The appearance-based methods that make use of standard low-resolution cameras are considered to be less invasive and, thus, more desirable in a large range of applications. Within the appearance-based...

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“…The head may be approximated as a 3D rigid object, so recovering the motion of the face is equivalent to recovering the motion of the 3D object. One typical model is a 3D cylinder head model, as seen in [1,6,7,19,20]. Ellipsoid models may also be used as in [2,8,9].…”

Section: Introductionmentioning

confidence: 99%

Occlusion robust multi-camera face tracking

Harguess¹,

Hu²,

Aggarwal³

2011

CVPR 2011 Workshops

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This paper presents a novel approach to object tracking by using multiple views to assist with handling occlusion which improves the overall tracking result. The approach is applied to face tracking using a 3D cylinder head model, but any 3D rigid object may be tracked using this approach. All cameras in the system are used to estimate a joint motion model of the face, which is updated at each frame. Self-occlusion is handled by a weighted mask that depends on the pose of the face. Full face occlusion is first detected automatically by measuring and comparing image histograms of the current tracking result and a face template. If an occlusion from a camera is reported, it is not used in the global tracking result of the face from the multicamera system. Experiments demonstrate that our method succeeds in tracking in both cases of self-occlusion and full face occlusion. Comparisons are made between single camera tracking, multi-camera tracking and occlusion robust multi-camera tracking using results from pose estimation. The performance of the occlusion robust multi-camera face tracking method is shown to produce more accurate estimates of the face pose and is able to estimate the face pose even under severe face occlusion.

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Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models

Cited by 75 publications

References 16 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

Combining Head Pose and Eye Location Information for Gaze Estimation

Occlusion robust multi-camera face tracking

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