Nonconvex regularizer and latent pattern based robust regression for face recognition

Sang, Xiaoshuang; Lu, Hong; Zhao, Qinghua; Zhang, Faen; Lu, Jianfeng

doi:10.1016/j.ins.2020.08.016

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…From the previous work, there are some observations: (1) Anchor-based face detectors [23,7,17,24,25] have demonstrated the most promising accuracy, particularly on the most challenging dataset Wider Face [26]. (2) Almost all the state-of-the-art(SOTA) anchor-based face detectors adopt multi-scale detection heads and deploy many facial specific modules for dense anchor design [9] and anchor mining [8,23,24], which may lead to anchor misalignment and poor design generality easily. (3) Although heatmap-based anchor-free detectors can achieve SOTA results on the generic object detection [27,28,29], yet most heatmap-based face detectors [30,15] suffer from low face detection accuracy [13].…”

Section: : How To Improve the Accuracy Of Heatmap-basedmentioning

confidence: 99%

“…We set Gaussian radius with the overlaps of predicted bounding box and the ground-truth adaptively instead of same standard deviation. Face classification loss L center is calculated by the focal loss [29] as in equation (2).…”

Section: Multi-task Face and Landmark Detectionmentioning

confidence: 99%

“…Face detection and facial landmark detection are two essential and fundamental steps in many facial analysis applications, such as recognition [1,2], generation [3], manipulation [4], tracking [5], attributes analysis [6], etc. The purpose of face detection is to locate facial bounding boxes on images, while facial landmark detection seeks to predict landmarks (i.e., nose, eye, etc.)…”

Section: Introductionmentioning

confidence: 99%

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HP-FLDet: A High Performance Joint Face and Landmark Detector

Liu,

Zhang,

Liu

et al. 2024

Preprint

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Face detection and landmark localization are usually the fundamental and important steps of facial analysis. The accuracy degradation of any task has impacts on the accuracy and robustness of the downstream analysis. In most facial analysis systems, face detection and landmark detection, as two independent tasks, are predicted sequentially with single-task detectors respectively, which suffer from the higher complexity and poorer consistency. In contrast, multi-task joint face and landmark detectors are more efficient and have more shared features. However, when two tasks are predicted in one model, it is difficult to improve the accuracy of all tasks simultaneously, due to the different requirements of the different targets to be detected. In our opinion, the multi-task detection model, integrating the perception ability of both scale variation and detailed texture information for face and landmark detection, is arduous but important in unconstrained settings. In this paper, a High-Performance joint Face and Landmark detector(HP-FLDet) is proposed, which can predict facial bounding boxes and landmarks in single stage and improve the accuracy of both face detection and landmark localization simultaneously. In this detector, the context inception block and scale-aware multi-task strategies are designed to resolve extreme scale variance and improve the perception ability of detailed texture information. It demonstrates superior performance on both face and landmark detection datasets including AFW, PASCAL Face, FDDB, Wider Face and AFLW. Especially, it achieves SOTA results on the most challenging WIDER FACE dataset with 0.970(Easy), 0.963(Medium), 0.921(Hard) mAP on the test subset, which outperforms all state-of-the-art rivals.

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