Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild

Feng, Zhen-Hua; Kittler, Josef; Awais, Muhammad; Huber, Patrik; Wu, Xiaojun

doi:10.1109/cvprw.2017.262

Cited by 32 publications

(23 citation statements)

References 56 publications

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“…in pose, expression, illumination, image blurring and occlusion. To this end, cascaded-regression-based approaches have been widely used, in which a set of weak regressors are cascaded to form a strong regressor [13,65,6,18,62,60,20]. However, the capability of cascaded regression is nearly saturated due to its shallow structure.…”

Section: Introductionmentioning

confidence: 99%

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

Feng

Kittler

Awais

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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368

212

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We present a new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs). We first compare and analyse different loss functions including L2, L1 and smooth L1. The analysis of these loss functions suggests that, for the training of a CNN-based localisation model, more attention should be paid to small and medium range errors. To this end, we design a piece-wise loss function. The new loss amplifies the impact of errors from the interval (-w, w) by switching from L1 loss to a modified logarithm function.To address the problem of under-representation of samples with large out-of-plane head rotations in the training set, we propose a simple but effective boosting strategy, referred to as pose-based data balancing. In particular, we deal with the data imbalance problem by duplicating the minority training samples and perturbing them by injecting random image rotation, bounding box translation and other data augmentation approaches. Last, the proposed approach is extended to create a two-stage framework for robust facial landmark localisation. The experimental results obtained on AFLW and 300W demonstrate the merits of the Wing loss function, and prove the superiority of the proposed method over the state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

Feng

Kittler

Awais

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Self Cite

368

212

View full text Add to dashboard Cite

show abstract

“…A powerful face detector can provide different pose, illumination, and scale. It returns a bounding box of the face that minimizes the background [12][13][14][15]. (B) Facial landmarks extractor: detects the facial landmarks such as eye centers, nose tip, and mouth corners.…”

Section: Related Workmentioning

confidence: 99%

Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

et al. 2020

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Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

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“…To obtain the geometry and texture information of 2D faces, a variety of methods have been developed during the past decades, e.g. Active Shape Model (ASM) [10], Active Appearance Model (AAM) [4], [5], Constrained Local Model (CLM) [11], [12] and Cascaded Regression (CR-) based facial landmark detection methods [13]- [17]. Among these algorithms, AAM is capable of jointly modelling the shape and texture information of faces.…”

Section: Related Workmentioning

confidence: 99%

A Unified Tensor-based Active Appearance Model

Feng

Kittler

Christmas

et al. 2019

ACM Trans. Multimedia Comput. Commun. Appl.

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Appearance variations result in many difficulties in face image analysis. To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces. For each type of face information, namely shape and texture, we construct a unified tensor model capturing all relevant appearance variations. This contrasts with the variationspecific models of the classical tensor AAM. To achieve the unification across pose variations, a strategy for dealing with selfoccluded faces is proposed to obtain consistent shape and texture representations of pose-varied faces. In addition, our UT-AAM is capable of constructing the model from an incomplete training dataset, using tensor completion methods. Last, we use an effective cascaded-regression-based method for UT-AAM fitting. With these advancements, the utility of UT-AAM in practice is considerably enhanced. As an example, we demonstrate the improvements in training facial landmark detectors through the use of UT-AAM to synthesise a large number of virtual samples. Experimental results obtained using the Multi-PIE and 300-W face datasets demonstrate the merits of the proposed approach.

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Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild

Cited by 32 publications

References 56 publications

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

A Unified Tensor-based Active Appearance Model

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