Localizing parts of faces using a consensus of exemplars

Belhumeur, Peter N.; Jacobs, David W.; Kriegman, David; Kumar, Neeraj

doi:10.1109/cvpr.2011.5995602

Cited by 577 publications

(773 citation statements)

References 19 publications

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“…Although the history of facial landmark localisation spans back many decades (Cootes et al , 2001, the ability to accurately recover facial landmarks on in-the-wild images has only become possible in recent years Papandreou and Maragos 2008;Saragih et al 2011;Cao et al 2014). Much of this progress can be attributed to the release of large annotated datasets of facial landmarks (Sagonas et al 2013b, a;Zhu and Ramanan 2012;Le et al 2012;Belhumeur et al 2013;Köstinger et al 2011) and very recently the area of facial landmark localisation has become extremely competitive with recent works including Xiong and De la Torre (2013), Ren et al (2014), Kazemi and Sullivan (2014), Zhu et al (2015), Tzimiropoulos (2015). For a recent evaluation of facial landmark localisation methods the interested reader may refer to the survey by Wang et al (2014) and to the results of the 300 W competition by Sagonas et al (2015).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…For fairness, we augment these non-frontal images by a horizontal flip, so that we get the same numbers of left and right images. 300W dataset is mainly made up of images from LFPW [39], HELEN [40], AFW [41] with 68 re-annotated landmarks. The 3148 images from training dataset are selected in our validation.…”

Section: Sign-correlation Partition Validationmentioning

confidence: 99%

Sign-Correlation Partition Based on Global Supervised Descent Method for Face Alignment

Zhang

Liu

Yang

et al. 2017

Computer Vision – ACCV 2016

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Abstract. Face alignment is an essential task for facial performance capture and expression analysis. As a complex nonlinear problem in computer vision, face alignment across poses is still not studied well. Although the state-of-the-art Supervised Descent Method (SDM) has shown good performance, it learns conflict descent direction in the whole complex space due to various poses and expressions. Global SDM has been presented to deal with this case by domain partition in feature and shape PCA spaces for face tracking and pose estimation. However, it is not suitable for the face alignment problem due to unknown ground truth shapes. In this paper we propose a sign-correlation subspace method for the domain partition of global SDM. In our method only one reduced low dimensional subspace is enough for domain partition, thus adjusting the global SDM efficiently for face alignment. Unlike previous methods, we analyze the sign correlation between features and shapes, and project both of them into a mutual sign-correlation subspace. Each pair of projected shape and feature keep sign consistent in each dimension of the subspace, so that each hyperoctant holds the condition that one general descent exists. Then a set of general descent directions are learned from the samples in different hyperoctants. Our sign-correlation partition method is validated in the public face datasets, which includes a range of poses. It indicates that our methods can reveal their latent relationships to poses. The comparison with state-of-the-art methods for face alignment demonstrates that our method outperforms them especially in uncontrolled conditions with various poses, while keeping comparable speed.

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“…These landmarks were used to drive the annotation process of the AFLW database with regards to facial bounding boxes [26]. Finally, other databases that can be used for training face detection algorithms are the LFPW [199], HELEN [200] and iBUG databases [197], since facial landmark annotations are provided by the database creators.…”

Section: Databases and Benchmarksmentioning

confidence: 99%