Face Alignment Via Component-Based Discriminative Search

Li, Lin; Xiao, Rong; Wen, Fang; Sun, Jian

doi:10.1007/978-3-540-88688-4_6

Cited by 124 publications

(73 citation statements)

References 17 publications

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“…a state-of-the-art face alignment algorithm [13] is first utilized to detect some predefined landmarks on both the training sketches and the testing photo. The chosen landmarks locate in regions where loss of structures often happens, especially on the face profile.…”

Section: Shape Priormentioning

confidence: 99%

Lighting and Pose Robust Face Sketch Synthesis

Zhang

Wang

Tang

2010

Computer Vision – ECCV 2010

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Abstract. Automatic face sketch synthesis has important applications in law enforcement and digital entertainment. Although great progress has been made in recent years, previous methods only work under well controlled conditions and often fail when there are variations of lighting and pose. In this paper, we propose a robust algorithm for synthesizing a face sketch from a face photo taken under a different lighting condition and in a different pose than the training set. It synthesizes local sketch patches using a multiscale Markov Random Field (MRF) model. The robustness to lighting and pose variations is achieved in three steps. Firstly, shape priors specific to facial components are introduced to reduce artifacts and distortions caused by variations of lighting and pose. Secondly, new patch descriptors and metrics which are more robust to lighting variations are used to find candidates of sketch patches given a photo patch. Lastly, a smoothing term measuring both intensity compatibility and gradient compatibility is used to match neighboring sketch patches on the MRF network more effectively. The proposed approach significantly improves the performance of the state-of-the-art method. Its effectiveness is shown through experiments on the CUHK face sketch database and celebrity photos collected from the web.

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Section: Shape Priormentioning

confidence: 99%

Lighting and Pose Robust Face Sketch Synthesis

Zhang

Wang

Tang

2010

Computer Vision – ECCV 2010

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“…In our case each regression function in the cascade efficiently estimates the shape from an initial estimate and the intensities of a sparse set of pixels indexed relative to this initial estimate. Our work builds on the large amount of research over the last decade that has resulted in significant progress for face alignment [9,4,13,7,15,1,16,18,3,6,19]. In particular, we incorporate into our learnt regression functions two key elements that are present in several of the successful algorithms cited and we detail these elements now.…”

Section: Introductionmentioning

confidence: 99%

One millisecond face alignment with an ensemble of regression trees

Kazemi

Sullivan

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

2,307

1,440

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This paper addresses the problem of Face Alignment for a single image. We show how an ensemble of regression trees can be used to estimate the face's landmark positions directly from a sparse subset of pixel intensities, achieving super-realtime performance with high quality predictions. We present a general framework based on gradient boosting for learning an ensemble of regression trees that optimizes the sum of square error loss and naturally handles missing or partially labelled data. We show how using appropriate priors exploiting the structure of image data helps with efficient feature selection. Different regularization strategies and its importance to combat overfitting are also investigated. In addition, we analyse the effect of the quantity of training data on the accuracy of the predictions and explore the effect of data augmentation using synthesized data.

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“…The shape parameters are locally searched using a linear regression function on the texture residual. In the past decade, various strategies [10,24,16] have been proposed for improving the performance of ASM and AAM. For instance, constrained local model (CLM) [9,26,30] extends ASM by modeling the non-rigid face as an ensemble of low dimensional independent patch experts.…”

Section: Related Workmentioning

confidence: 99%

Exemplar-Based Graph Matching for Robust Facial Landmark Localization

Zhou

Brandt

Lin

2013

2013 IEEE International Conference on Computer Vision

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Localizing facial landmarks is a fundamental step in facial image analysis. However, the problem is still challenging due to the large variability in pose and appearance, and the existence of occlusions in real-world face images. In this paper, we present exemplar-based graph matching (EGM), a robust framework for facial landmark localization. Compared to conventional algorithms, EGM has three advantages: (1) an affine-invariant shape constraint is learned online from similar exemplars to better adapt to the test face; (2) the optimal landmark configuration can be directly obtained by solving a graph matching problem with the learned shape constraint; (3) the graph matching problem can be optimized efficiently by linear programming. To our best knowledge, this is the first attempt to apply a graph matching technique for facial landmark localization. Experiments on several challenging datasets demonstrate the advantages of EGM over state-of-the-art methods.

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Face Alignment Via Component-Based Discriminative Search

Cited by 124 publications

References 17 publications

Lighting and Pose Robust Face Sketch Synthesis

Lighting and Pose Robust Face Sketch Synthesis

One millisecond face alignment with an ensemble of regression trees

Exemplar-Based Graph Matching for Robust Facial Landmark Localization

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