Improving Face Detection Performance by Skin Detection Post-Processing

Lucena, Oeslle; Oliveira, Ítalo de Pontes; Veloso, Luciana; Pereira, Eanes Torres

doi:10.1109/sibgrapi.2017.46

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…The importance of improving the rate of detection by enhancing performance of techniques and methods has been highlighted by [126]. The researchers in the current study [46], [87], [95], [135] propose the combination of color spaces for skin detection so as to enhance detection. The research is motivated by the fact that the existing skin detectors are slow in terms of processing the captured image [31].…”

Section: ) Motivation Related To Performancementioning

confidence: 97%

“…Bodhi and Naveen [11] and Ban et al [130] focuses on pre-processing technique in face detection and facial feature. And the other papers focused on the techniques in like fuzzy rule-based [6], knowledgebased method for segmentation [131], artificial neural net- work classifier [132], by the Gaussian Mixture Model [133], Genetic Algorithms for finding optimal limits for color components [134], using a straightforward and low complex skin percentage to enhanced weak detectors performance [135], and Using self-organizing fuzzy network with support vector learning [136]. Sattiraju et al [137] employed combination of Skin based background removal.…”

Section: B: Face Detectionmentioning

confidence: 99%

“…The application of skin detection is required in several applications such as surveillance [87], medical [45], forensics [135], authentication and security [150]. As noted by the researcher in [168], forensic investigation is key to the prohibition of pornographic video and image usage in media.…”

Section: ) Motivation Related To Applicationmentioning

confidence: 99%

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The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

et al. 2019

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This paper review and analysis the literature on skin detector (SD), in order to establish the coherent taxonomy and figure out the gap on this pivotal research area. An extensive search is conducted to identify articles that deal with skin detection, skin segmentation, skin tone detector, and skin recognition issues, related techniques are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore, and Web of Science databases are checked for articles on skin detector. A total of 2803 papers are collected from 2007 to February 2018. The set comprised 173 articles. The largest portion of the papers (n = 158/173) = 91% belong to Development and Design, that is aimed to develop an approach for skin classifier into skin and non-skin. A sum total of (n = 5/173) = 3% of the papers belong to Evaluation and Framework, (n = 10/173) = 6% papers was categorized as Comparative Study. This paper discusses the open challenges, motivations and recommendations of the related works. Furthermore, state-of-the-art is a step to demonstrate the novelty of the presented study by conducted a statistical analysis for previous studies such as (Dataset, Color spaces, features, image type, and Classification techniques) as a future direction for other researchers who are interested in SD.

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Section: ) Motivation Related To Performancementioning

confidence: 97%

Section: B: Face Detectionmentioning

confidence: 99%

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The Landscape of Research on Skin Detectors: Coherent Taxonomy, Open Challenges, Motivations, Recommendations and Statistical Analysis, Future Directions

et al. 2019

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“…Therefore, the skin color feature increased the positive detection rate. Lucena et al applied the skin color feature in the post-processing method to improve the performance of face detection [25]. Subsequently, Nusirwan et al [26] combine several color tones of human skin to achieve a higher skin detection rate.…”

Section: Related Workmentioning

confidence: 99%

“…Lucena et al. applied the skin color feature in the post-processing method to improve the performance of face detection [ 25 ]. Subsequently, Nusirwan et al.…”

Section: Related Workmentioning

confidence: 99%

Multi-audience tracking with RGB-D camera on digital signage

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Koo

et al. 2020

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Digital signage is widely utilized in digital-out-of-home (DOOH) advertising for marketing and business. Recently, the combination of the digital camera and digital signage enables the advertiser to gather the audience demographic for audience measurement. Audience measurement is useful for the advertiser to understand the audience's behavior and improve their business strategies. When an audience is facing the digital display, the vision-based DOOH system will process the audience's face and broadcast a personalized advertisement. Most of the digital signage is available in an uncontrolled environment of public areas. Thus, it poses two main challenges for the vision-based DOOH system to track the audience's movement, which are multiple adjacent faces and occlusion by passer-by. In this paper, a new framework is proposed to combine the digital signage with a depth camera for tracking multi-face in the three-dimensional (3D) environment. The proposed framework extracts the audience's face centroid position (x, y) and depth information (z) and plots into the aerial map to simulate the audience's movement that is corresponding to the real-world environment. The advertiser can further measure the advertising effectiveness through the audience's behavior.

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