Lightweight and Effective Facial Landmark Detection using Adversarial Learning with Face Geometric Map Generative Network

Lee, Hong Joo; Kim, Seong Tae; Lee, Hakmin; Ro, Yong Man

doi:10.1109/tcsvt.2019.2897243

Cited by 20 publications

(6 citation statements)

References 30 publications

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“…Regarding the face of a human as a biometric, the related tasks can be face detection [62], [63], [64], [65], face alignment [66], [67], face recognition [68], face tracking [69], [70], face classification/verification [71], and face landmarks extraction [72], [73], [74]. Fingerprint [75], [76], [77], palmprint [78] and iris/gaze [79], [80] are mainly used for user's identification tasks due to their uniqueness for each person.…”

Section: Human Centric Perceptionmentioning

confidence: 99%

Empowering cyberphysical systems of systems with intelligence

Nousias¹,

Piperigkos²,

Arvanitis³

et al. 2021

Preprint

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Cyber Physical Systems have been going into a transition phase from individual systems to a collecttives of systems that collaborate in order to achieve a highly complex cause, realizing a system of systems approach. The automotive domain has been making a transition to the system of system approach aiming to provide a series of emergent functionality like traffic management, collaborative car fleet management or large-scale automotive adaptation to physical environment thus providing significant environmental benefits (e.g air pollution reduction) and achieving significant societal impact. Similarly, large infrastructure domains, are evolving into global, highly integrated cyber-physical systems of systems covering all parts of the value chain. In practice, there are significant challenges in CPSoS applicability and usability to be addressed, i.e. even a small CPSoS such as a car consists several subsystems Decentralization of CPSoS appoints tasks to individual CPSs within the System of Systems. CPSoSs are heterogenous systems. They comprise of various, autonomous, CPSs, each one of them having unique performance capabilities, criticality level, priorities and pursued goals. all CPSs must also harmonically pursue systembased achievements and collaborate in order to make system-ofsystem based decisions and implement the CPSoS functionality. This survey will provide a comprehensive review on current best practices in connected cyberphysical systems. The basis of our investigation is a dual layer architecture encompassing a perception layer and a behavioral layer. Perception algorithms with respect to scene understanding (object detection and tracking, pose estimation), localization mapping and path planning are thoroughly investigated. Behavioural part focuses on decision making and human in the loop control.

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Section: Human Centric Perceptionmentioning

confidence: 99%

Empowering cyberphysical systems of systems with intelligence

Nousias¹,

Piperigkos²,

Arvanitis³

et al. 2021

Preprint

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show abstract

“…Face analysis has improved considerably in various tasks, including detection [1,2], recognition [3][4][5], and estimation [6,7], with several models achieving remarkable accuracy and efficiency. This progress is attributed to the development of various network structures and the availability of a large number of face datasets for training.…”

Section: Introductionmentioning

confidence: 99%

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation

Lee,

Jang,

Bae

et al. 2024

Sensors

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Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.

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“…Xiong et al [12] proposed the Gaussian vector to reduce the model complexity. Lee et al [13] exploited the advantage of geometric prior-generative adversarial network to design an associated learning framework for facial landmark detection. Wang et al [14] proposed an efficient 3D face reconstruction method by leveraging Mo-bileNet [15].…”

Section: Introductionmentioning

confidence: 99%

Robust and Efficient Facial Landmark Localization

Hu¹,

Wang

Tao³

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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Facial landmark localization is an essential component for facial recognition which is a vital technology for patients tracking in the situation of a global pandemic of COVID-19. Parameter amount and computation cost are two key factors affecting the application of facial landmark localization. In addition, the intrinsic imbalance of head pose in existing datasets has a great impact on the model generalization. To address these problems, this paper proposed a lightweight model LiteFace with multi-knowledge distillation and a pose-aware resampling strategy. Specifically, we proposed to extract multi-scale features and enhance the receptive filed of network by delicate model design to obtain an efficient architecture and improved the performance of the lightweight model by multi-knowledge distillation which combines knowledge distillation and mutual learning in a two-stage manner. To further improve the model performance and boost the generalization, we proposed a pose-aware resampling strategy that generates samples with different head poses and utilized the LaPa dataset to generate masked face images to increase data diversity. We conducted extensive ablation studies on the model design, multi-knowledge distillation and resampling strategy. The proposed method achieves 1.93% and 3.04% NME on the JD-landmark-mask val and test dataset respectively. Finally, LiteFace wins the third place in the 3rd Grand Challenge of 106-Point Facial Landmark Localization.

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Lightweight and Effective Facial Landmark Detection using Adversarial Learning with Face Geometric Map Generative Network

Cited by 20 publications

References 30 publications

Empowering cyberphysical systems of systems with intelligence

Empowering cyberphysical systems of systems with intelligence

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation

Robust and Efficient Facial Landmark Localization

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