Adaptive skin detection using face location and facial structure estimation

Luo, Yong; Guan, Yuanyuan

doi:10.1049/iet-cvi.2016.0295

Cited by 16 publications

(12 citation statements)

References 33 publications

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“…Yaman et al [20] proposed a framework to detect faces by utilizing histogram-based feature extraction with random subspace and voting ensemble learners. Luo et al [21] proposed a face location algorithm which is developed to extract face regions with a high proportion of skin. This type of method increases the speed of the operation.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Yang

Min

et al. 2019

Applied Sciences

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Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.

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Section: Related Workmentioning

confidence: 99%

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Yang

Min

et al. 2019

Applied Sciences

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“…Dynamic Region growing skin detection techniques is one of approaches is widely used [94] paper in dynamic skin detection present a dynamic threshold approach by applied fix threshold classification (fix decision boundaries) in skin to differentiate between skin pixels and non-skin pixels. The paper in [95] aimed to solve urgent issue for adaptive skin detection such as ( accurate facial skin extraction and reliability ) by used facial structure estimation to reduce the impact of non-skin factor in skin color. While the paper in [96] proposed method to allow skin region to grow dynamically which is Dynamic Region Growing (DRG).…”

Section: B: Region Basedmentioning

confidence: 99%

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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“…For reliability, some researchers focus on improving face detection methods, such as extracting hybrid features and improving classifiers [5]. However, when pedestrians are far away from the cameras, the target domain mainly consists of low‐resolution images, while the source domain mainly contains high‐resolution images.…”

Section: Introductionmentioning

confidence: 99%

Real‐time face recognition based on pre‐identification and multi‐scale classification

Min

Fan

et al. 2019

IET Computer Vision

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In face recognition, searching a person's face in the whole picture is generally too time-consuming to ensure highdetection accuracy. Objects similar to the human face or multi-view faces in low-resolution images may result in the failure of face recognition. To alleviate the above problems, a real-time face recognition method based on pre-identification and multiscale classification is proposed in this study. The face area is segmented based on the proportion of human faces in the pedestrian area to reduce the search range, and faces can be robustly detected in complicated scenarios such as heads moving frequently or with large angles. To accurately recognise small-scale faces, the authors propose the multi-scale and multi-channel shallow convolution network, which combines a multi-scale mechanism on the feature map with a multi-channel convolution network for real-time face recognition. It performs face matching only in the pre-identified face areas instead of the whole image, therefore it is more efficient. Experimental results showed that the proposed real-time face recognition method detects and recognises faces correctly, and outperforms the existing methods in terms of effectiveness and efficiency.

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Adaptive skin detection using face location and facial structure estimation

Cited by 16 publications

References 33 publications

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

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

Real‐time face recognition based on pre‐identification and multi‐scale classification

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