Pixel encoding for unconstrained face detection

Sawat, Dattatray D.; Hegadi, Rajendra; Garg, Lalit; Hegadi, Ravindra S.

doi:10.1007/s11042-020-08800-1

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…C1 C2 C3 C4 C5 C6 C7 Total [Zeng et al 2019] [Zhou et al 2020 1 1 1 1 1 1 1 7 [Liao et al 2016] 1 0.5 1 1 1 1 1 6.5 [Chen et al 2018] [Shi et al 2018] [Zhang et al 2017a] [Zhou et al 2022] [Zhang et al 2020] [Zhu et al 2018] [Zhang et al 2019b 1 1 1 0 1 1 1 6 [Liu and Levine 2017] [Zhang et al 2019a] [Triantafyllidou et al 2018] [Micheal and Geetha 2017] [Li et al 2020] [Jiang et al 2018] [Sawat et al 2020] [Wang et al 2019] 1 1 0.5 0 1 1 1 5.5 [Zheng et al 2016] [Alafif et al 2017] [Deng and Xie 2017b] [Zakaria et al 2018] [Nanni et al 2019] 1 1 0.5 1 0 1 1 5.5 [Deng and Xie 2017a] 1 1 1 0 0.5 1 1 5.5 [Bai and Ghanem 2017] 0.5 1 1 0 1 1 1 5.5 1 0.5 1 0 1 1 1 5.5 [Lin et al 2016] 1 1 1 0 1 0 1 5 [Ge et al 2017] [El-Barkouky et al 2014] 1 1 0.5 0 0.5 1 1 5 [Yan et al 2014] 1 0.5 0.5 0 1 1 1 5 [Nguyen et al 2015b] 0.5 1 1 0 0.5 1 1 5 [Li et al 2017a] 1 1 0.5 0 1 0 1 4.5 [Yang et al 2018] 1 0.5 1 0 0 1 1 4.5 [Shu et al 2017] 0.5 1 0.5 0 0.5 1 1 4.5 [Lv et al 2016] 0.5 1 0.5 0 0.5 1 1 4.5 [Chai et al 2014] 1 0.5 0.5 0 0.5 1 1 4.5 [Marčetić et al 2016] 1 0.5 1 0 0.5 1 0 4 [Li et al ...…”

Section: Referencementioning

confidence: 99%

Detecting faces in specific scenarios: Systematic Literature Review

Dias,

Ivamoto,

Lima

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Facial detection is a base component for multiple applications in the fields of biometrics, surveillance, human-robot interaction and others. Although significant progress has been made in the field over the past decade, there are still gaps to be addressed, particularly in specific scenarios as the presence of partial occlusion, variations of lighting, pose, and scale among others. This work aims to provide a comprehensive evaluation of recent studies on facial detection in the wild through a systematic literature review. The review includes a focus on the use of scenario-specific information within the field. A total of forty-five papers were analyzed to provide an overview of the field, incorporating information on scenarios.

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Section: Referencementioning

confidence: 99%

Detecting faces in specific scenarios: Systematic Literature Review

Dias,

Ivamoto,

Lima

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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“…NPDFace [15] proposes normalized pixel difference feature to address challenges in unconstrained face detection, such as arbitrary pose and heavy occlusion. Sawat et al [16] adpots hand-crafted as well as visual features together for improving robustness in face detection.…”

Section: Related Workmentioning

confidence: 99%

Wide Aspect Ratio Matching for Robust Face Detection

Luo¹,

Li²,

Zhang³

2021

Preprint

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Recently, anchor-based methods have achieved great progress in face detection. Once anchor design and anchor matching strategy determined, plenty of positive anchors will be sampled. However, faces with extreme aspect ratio always fail to be sampled according to standard anchor matching strategy. In fact, the max IoUs between anchors and extreme aspect ratio faces are still lower than fixed sampling threshold. In this paper, we firstly explore the factors that affect the max IoU of each face in theory. Then, anchor matching simulation is performed to evaluate the sampling range of face aspect ratio. Besides, we propose a Wide Aspect Ratio Matching (WARM) strategy to collect more representative positive anchors from ground-truth faces across a wide range of aspect ratio. Finally, we present a novel feature enhancement module, named Receptive Field Diversity (RFD) module, to provide diverse receptive field corresponding to different aspect ratios. Extensive experiments show that our method can help detectors better capture extreme aspect ratio faces and achieve promising detection performance on challenging face detection benchmarks, including WIDER FACE and FDDB datasets.

show abstract

Pixel encoding for unconstrained face detection

Cited by 2 publications

References 31 publications

Detecting faces in specific scenarios: Systematic Literature Review

Detecting faces in specific scenarios: Systematic Literature Review

Wide Aspect Ratio Matching for Robust Face Detection

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