Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the r...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lumini, Alessandra [verfasserIn] Nanni, Loris [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Skin classification Skin detection Skin segmentation Skin database

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 160
Übergeordnetes Werk:	volume:160

DOI / URN:	10.1016/j.eswa.2020.113677

Katalog-ID:	ELV004689984

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520			\|a Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets.
650		4	\|a Skin classification
650		4	\|a Skin detection
650		4	\|a Skin segmentation
650		4	\|a Skin database
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author_variant	a l al l n ln
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allfields	10.1016/j.eswa.2020.113677 doi (DE-627)ELV004689984 (ELSEVIER)S0957-4174(20)30501-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Lumini, Alessandra verfasserin (orcid)0000-0003-0290-7354 aut Fair comparison of skin detection approaches on publicly available datasets 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets. Skin classification Skin detection Skin segmentation Skin database Nanni, Loris verfasserin (orcid)0000-0002-3502-7209 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 160 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:160 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 160
spelling	10.1016/j.eswa.2020.113677 doi (DE-627)ELV004689984 (ELSEVIER)S0957-4174(20)30501-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Lumini, Alessandra verfasserin (orcid)0000-0003-0290-7354 aut Fair comparison of skin detection approaches on publicly available datasets 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets. Skin classification Skin detection Skin segmentation Skin database Nanni, Loris verfasserin (orcid)0000-0002-3502-7209 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 160 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:160 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 160
allfields_unstemmed	10.1016/j.eswa.2020.113677 doi (DE-627)ELV004689984 (ELSEVIER)S0957-4174(20)30501-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Lumini, Alessandra verfasserin (orcid)0000-0003-0290-7354 aut Fair comparison of skin detection approaches on publicly available datasets 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets. Skin classification Skin detection Skin segmentation Skin database Nanni, Loris verfasserin (orcid)0000-0002-3502-7209 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 160 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:160 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 160
allfieldsGer	10.1016/j.eswa.2020.113677 doi (DE-627)ELV004689984 (ELSEVIER)S0957-4174(20)30501-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Lumini, Alessandra verfasserin (orcid)0000-0003-0290-7354 aut Fair comparison of skin detection approaches on publicly available datasets 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets. Skin classification Skin detection Skin segmentation Skin database Nanni, Loris verfasserin (orcid)0000-0002-3502-7209 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 160 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:160 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 160
allfieldsSound	10.1016/j.eswa.2020.113677 doi (DE-627)ELV004689984 (ELSEVIER)S0957-4174(20)30501-7 DE-627 ger DE-627 rda eng 004 DE-600 54.72 bkl Lumini, Alessandra verfasserin (orcid)0000-0003-0290-7354 aut Fair comparison of skin detection approaches on publicly available datasets 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets. Skin classification Skin detection Skin segmentation Skin database Nanni, Loris verfasserin (orcid)0000-0002-3502-7209 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 160 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:160 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz AR 160
language	English
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container_title	Expert systems with applications
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author	Lumini, Alessandra
spellingShingle	Lumini, Alessandra ddc 004 bkl 54.72 misc Skin classification misc Skin detection misc Skin segmentation misc Skin database Fair comparison of skin detection approaches on publicly available datasets
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topic_title	004 DE-600 54.72 bkl Fair comparison of skin detection approaches on publicly available datasets Skin classification Skin detection Skin segmentation Skin database
topic	ddc 004 bkl 54.72 misc Skin classification misc Skin detection misc Skin segmentation misc Skin database
topic_unstemmed	ddc 004 bkl 54.72 misc Skin classification misc Skin detection misc Skin segmentation misc Skin database
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title	Fair comparison of skin detection approaches on publicly available datasets
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title_full	Fair comparison of skin detection approaches on publicly available datasets
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title_sort	fair comparison of skin detection approaches on publicly available datasets
title_auth	Fair comparison of skin detection approaches on publicly available datasets
abstract	Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets.
abstractGer	Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets.
abstract_unstemmed	Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. In this work, we investigate the most recent researches in this field, and we propose a fair comparison among approaches using several different datasets.
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title_short	Fair comparison of skin detection approaches on publicly available datasets
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up_date	2024-07-06T23:50:10.662Z
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Skin detection is a challenging problem which has drawn extensive attention from the research community in the context of expert and intelligent systems, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In the recent era, the success of deep convolutional neural network (CNN) has strongly influenced the field of image segmentation and gave us various successful models to date. Anyway, due to the lack of large ground truth for skin detection only few works have addressed the skin detection problem using CNN models. 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Fair comparison of skin detection approaches on publicly available datasets

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