Integrated artificial intelligence algorithm for skin detection

Bush, Idoko John; Abiyev, Rahib H.; Ma’aitah, Mohammad Khaleel Sallam; Altıparmak, Hamit

doi:10.1051/itmconf/20181602004

Cited by 28 publications

(4 citation statements)

References 31 publications

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“…The tactile sensor can be used as a new technology that can be added to the places that the intruder can use and touch as the system identifies person who touches this piece Skin directly by examining the texture. (Toshinobu and others) worked on developing this idea based on the intelligent particle swarm algorithm [27], in [13] Idoko John Bush and others used Neuro Fuzzy Inference System to detect the skin color of person, as for [70] feed forward neural networks algorithm is used for same purpose. Table 13 is the paper properties for last five years:…”

Section: Skin Texturementioning

confidence: 99%

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Different Biometrical Features for Detecting Human Intrusion Using Artificial Intelligence: Literature Review

Younis¹

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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Many cases of theft and property trespass in addition to crimes occur in the world after these people break into people's homes and buildings illegally, so this article aims to shed light on most of the smart methods and computer technologies used in identifying people that help to reduce these crimes. Where the diversity of biometric traits was relied upon, such as fingerprint, handprint, ear, face, texture, some deformations characteristic of people, eye, footprints, DNA analysis and other important biometric traits. Also, many intelligent algorithms were used to identify these traits.

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Section: Skin Texturementioning

confidence: 99%

“…Algorithm Accuracy [27] A live dataset 70 Soft Robotics 90% [13] A live dataset 100 Fuzzy 89%_90.10%…”

Section: Number Of Imagesmentioning

confidence: 99%

Different Biometrical Features for Detecting Human Intrusion Using Artificial Intelligence: Literature Review

Younis¹

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…In [9], an adaptive neuro-fuzzy inference system (ANFIS) for skin/non-skin pixels detection was proposed. [25] presented a new threshold based on a combination of RGB, HSV and YCbCr values for skin/non-skin pixels detection.…”

Section: Pbp (Pw) Operationsmentioning

confidence: 99%

PKT: fast color-based spatial model for human skin detection

Samson

2021

Multimed Tools Appl

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We present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

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“…The skin detection is a branch of digital image processing that have gained quite much importance and is continued to do so. The rapid technological growth causing the implementation of skin detection widely so much making it a complete domain inherited with numerous contraptions, like fingerprint recognition, face detection and recognition, lip reading, pattern recognition, artificial intelligence and much more [1]. Currently it is also subjected to be playing a vital role in various security systems for the reduction in criminal activities, better law enforcement like intelligent traffic video surveillance that uses the skin detection techniques for human facial recognition to identify criminals etc.…”

Section: Introductionmentioning

confidence: 99%

Implementation of NOGIE and NOWGIE for Human Skin Detection

Aftab¹,

Javed²,

Bilal³

et al. 2018

ijacsa

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The Digital image processing is one of the most widely implemented fields worldwide. The most applied applications of digital image processing are facial recognition, finger print recognition, medical imaging, law enforcement, cyber-crime investigation, identification of various diseases and criminals, etc. The subject to be discussed in this article is skin detection. Skin detection has solved many serious problems related to digital image process. It is one of the main features in making an intelligent image processing system. The proposed methodology conducts an improved and well enhanced skin detection, the skin and non-skin parts are divided from an input image or video, noise is removed, HSV is applied which also acts as a color model that generates more better results in accordance to RGB or YCbCr for skin and face identification. The algorithms, NOGIE (Noise Object Global Image Enhancement) and NOWGIE (Noise Object with Global Image Enhancement) are applied separately on the input and the results can be compared for better perception and understanding of the applied skin detection techniques, the skin parts are highlighted as "White" while the Non-skin parts are highlighted as "Black". The results are different NOWGIE gives better results than the NOGIE due to the image enhancement technique. This methodology is subjected to be implemented in special security drones for the identification of suspects, terrorists and spy's the algorithms provides the ability to detect humans from a non-skin background making an autonomous and excellent security system.

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Integrated artificial intelligence algorithm for skin detection

Cited by 28 publications

References 31 publications

Different Biometrical Features for Detecting Human Intrusion Using Artificial Intelligence: Literature Review

Different Biometrical Features for Detecting Human Intrusion Using Artificial Intelligence: Literature Review

PKT: fast color-based spatial model for human skin detection

Implementation of NOGIE and NOWGIE for Human Skin Detection

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