Convolutional aggregation of local evidence for large pose face alignment

Bulat, Adrian; Tzimiropoulos, Georgios

doi:10.5244/c.30.86

Cited by 59 publications

(65 citation statements)

References 26 publications

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“…Comprehensive results in Sec. 4 have shown that the more fusion we conducted to the baseline network, the better performance we can get. Input image fusion.…”

Section: Boundary-aware Landmarks Regressormentioning

confidence: 98%

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Qian

Yang

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

452

526

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We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundaryaware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. Dataset and model will be publicly available at https://wywu. github.io/projects/LAB/LAB.html

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“…Comprehensive results in Sec. 4 have shown that the more fusion we conducted to the baseline network, the better performance we can get. Input image fusion.…”

Section: Boundary-aware Landmarks Regressormentioning

confidence: 98%

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Qian

Yang

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

452

526

View full text Add to dashboard Cite

show abstract

“…Finally, it is worth mentioning the advent of facial landmark localisation using Deep Learning [5], [30], [52]. However, despite achieving impressive accuracy, these techniques are far from being able to operate at real-time speed.…”

Section: Prior Work On Face Trackingmentioning

confidence: 99%

A Functional Regression Approach to Facial Landmark Tracking

Sánchez-Lozano

Tzimiropoulos

Martínez

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 0162-8828 (c) 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-Linear regression is a fundamental building block in many face detection and tracking algorithms, typically used to predict shape displacements from image features through a linear mapping. This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker. Contrary to prior work in Functional Regression, in which B-splines or Fourier series were used, we propose to approximate the input space by its first-order Taylor expansion, yielding a closed-form solution for the continuous domain of displacements. We then extend the continuous least squares problem to correlated variables, and demonstrate the generalisation of our approach. We incorporate Continuous Regression into the cascaded regression framework, and show its computational benefits for both training and testing. We then present a fast approach for incremental learning within Cascaded Continuous Regression, coined iCCR, and show that its complexity allows real-time face tracking, being 20 times faster than the state of the art. To the best of our knowledge, this is the first incremental face tracker that is shown to operate in real-time. We show that iCCR achieves state-of-the-art performance on the 300-VW dataset, the most recent, large-scale benchmark for face tracking.

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“…A common approach to facial landmark detection problem is to learn a regression model [31,64,75,5,73,7,63]. Many of them leverage deep CNN to learn facial features and regressors in an end-to-end fashion [51,31,73] Another category of facial landmark detection methods takes the advantages of end-to-end training from deep CNN model to learn robust heatmap for facial landmark detection [27,57,6,4]. Wei et al [27] and Newell et al [34] take the location with the highest response on the heatmap as the coordinate of the corresponding landmarks.…”

Section: Facial Landmark Detectionmentioning

confidence: 99%

Style Aggregated Network for Facial Landmark Detection

Dong

Yan

Ouyang

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

292

212

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Recent advances in facial landmark detection achieve success by learning discriminative features from rich deformation of face shapes and poses. Besides the variance of faces themselves, the intrinsic variance of image styles, e.g., grayscale vs. color images, light vs. dark, intense vs. dull, and so on, has constantly been overlooked. This issue becomes inevitable as increasing web images are collected from various sources for training neural networks. In this work, we propose a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection. Our method transforms original face images to style-aggregated images by a generative adversarial module. The proposed scheme uses the style-aggregated image to maintain face images that are more robust to environmental changes. Then the original face images accompanying with style-aggregated ones play a duet to train a landmark detector which is complementary to each other. In this way, for each face, our method takes two images as input, i.e., one in its original style and the other in the aggregated style. In experiments, we observe that the large variance of image styles would degenerate the performance of facial landmark detectors. Moreover, we show the robustness of our method to the large variance of image styles by comparing to a variant of our approach, in which the generative adversarial module is removed, and no style-aggregated images are used. Our approach is demonstrated to perform well when compared with state-of-the-art algorithms on benchmark datasets AFLW and 300-W. Code is publicly available on GitHub: https://github.com/D-X-Y/SAN

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Convolutional aggregation of local evidence for large pose face alignment

Cited by 59 publications

References 26 publications

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

A Functional Regression Approach to Facial Landmark Tracking

Style Aggregated Network for Facial Landmark Detection

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