SDD: A skin detection dataset for training and assessment of human skin classifiers

Mahmoodi, Mohammad Reza; Sayedi, Sayed Masoud; Karimi, Fariba; Fahimi, Zahra; Rezai, Vahid; Mannani, Zahra

doi:10.1109/kbei.2015.7436024

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…The dataset of Feeval [24] is the only one available; however, this dataset is not qualitative enough with imprecise ground truths. Additionally, Mahmoodi et al [8] used their self-made SDD dataset in [25] including 33 videos. Yet, their dataset was not made available for other researchers.…”

Section: The Experimental Results and Discussionmentioning

confidence: 99%

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Thabet

Khalid

Sulaiman

et al. 2017

Advances in Multimedia

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Recent years have witnessed renewed interest in developing skin segmentation approaches. Skin feature segmentation has been widely employed in different aspects of computer vision applications including face detection and hand gestures recognition systems. This is mostly due to the attractive characteristics of skin colour and its effectiveness to object segmentation. On the contrary, there are certain challenges in using human skin colour as a feature to segment dynamic hand gesture, due to various illumination conditions, complicated environment, and computation time or real-time method. These challenges have led to the insufficiency of many of the skin color segmentation approaches. Therefore, to produce simple, effective, and cost efficient skin segmentation, this paper has proposed a skin segmentation scheme. This scheme includes two procedures for calculating generic threshold ranges in Cb-Cr colour space. The first procedure uses threshold values trained online from nose pixels of the face region. Meanwhile, the second procedure known as the offline training procedure uses thresholds trained out of skin samples and weighted equation. The experimental results showed that the proposed scheme achieved good performance in terms of efficiency and computation time.

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Section: The Experimental Results and Discussionmentioning

confidence: 99%

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Thabet

Khalid

Sulaiman

et al. 2017

Advances in Multimedia

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“…VT-AAST [ 102 ] is a color-image database for benchmarking face detection and includes 66 images with precise ground truth.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

et al. 2023

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Skin detection involves identifying skin and non-skin areas in a digital image and is commonly used in various applications, such as analyzing hand gestures, tracking body parts, and facial recognition. The process of distinguishing between skin and non-skin regions in a digital image is widely used in a variety of applications, ranging from hand-gesture analysis to body-part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches, using several different datasets. The main contributions of this work are (i) a comprehensive review of the literature on approaches to skin-color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; and (iii) a testing protocol for evaluating and comparing different skin-detection approaches. Moreover, we propose an ensemble of convolutional neural networks and transformers that obtains a state-of-the-art performance.

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“…• SDD [101] contains 21,000 images, some images taken from a video and some others taken from a popular face dataset with different lighting conditions and with different skin colors of people around the world.…”

Section: Skin Detection Evaluation: Datasetsmentioning

confidence: 99%

A Standardized Approach for Skin Detection: Analysis of the Literature and Case-Studies

Nanni¹,

Loreggia²,

Lumini³

et al. 2022

Preprint

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Skin detection, the process of distinguishing between skin and non-skin regions in a digital image, is widely used in a variety of applications ranging from hand gesture analysis to body part tracking to facial recognition. Skin detection is a challenging problem that has received a lot of attention from experts and proposals from the research community in the context of intelligent systems, but the lack of common benchmarks and unified testing protocols has hampered fairness among approaches. Comparisons are very difficult. Recently, the success of deep neural networks has had a major impact on the field of image segmentation detection, resulting in various successful models to date. In this work, we survey the most recent research in this field and propose fair comparisons between approaches using several different datasets. The main contributions of this work are: (i) a comprehensive literature review of approaches to skin color detection and a comparison of approaches that may help researchers and practitioners choose the best method for their application; (ii) a comprehensive list of datasets that report ground truth for skin detection; (iii) a framework for evaluating and combining different skin detection approaches. Moreover, we proposed an ensemble of convolutional neural networks and transformers that obtains state of the art performance. All the code is made publicly available at https://github.com/LorisNanni

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SDD: A skin detection dataset for training and assessment of human skin classifiers

Cited by 6 publications

References 29 publications

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

Low Cost Skin Segmentation Scheme in Videos Using Two Alternative Methods for Dynamic Hand Gesture Detection Method

A Standardized Approach for Skin Detection: Analysis of the Literature and Case Studies

A Standardized Approach for Skin Detection: Analysis of the Literature and Case-Studies

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