Abstract

Skin detection is a well-studied area in color imagery and is useful in a number of scenarios to include search and rescue and computer vision. Most approaches focus on color imagery due to cost and availability. Many of the visible-based approaches do well at detecting skin (above 90%) but they tend to have relatively high false-alarm rates (8%–15%). This article presents a novel feature space for skin detection in visible and near infrared portions of the electromagnetic spectrum. The features are derived from known spectral absorption of skin constituents to include hemoglobin, melanin, and water as well as scattering properties of the dermis. Fitting a Gaussian mixture to skin and background distributions and using a likelihood ratio test detector, the features presented here show dominating performance when comparing receiver-operating characteristic curves (ROCs) and statistically significant improvement when comparing equal error rates and area under the ROC (AUC). A detection/false-alarm probability of 98.6%/1.1% is achieved for the averaged equal error rate (EER). EER values for the proposed feature space show a 5.6%–11.2% increase in detection probability with a 6.0%–11.6% decrease in false-alarm probability compared to well performing color-based features. The AUC shows a 0.034–0.173 increase in total area under the curve compared to well performing color-based features.

Full Article  |  PDF Article
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References

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2013 (2)

J. C. SanMiguel and S. Suja, “Skin detection by dual maximization of detectors agreement for video monitoring,” Pattern Recogn. Lett. 34, 2102–2109 (2013).
[Crossref]

S. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58, R37–R61 (2013).
[Crossref]

2010 (1)

C. Doukim, J. Dargham, A. Chekima, and S. Omatu, “Combining neural networks for skin detection,” Signal Image Process. 1, 1–11 (2010).

2007 (1)

P. Matts, P. Dykes, and R. Marks, “The distribution of melanin in skin determined in vivo,” Br. J. Dermatol. 156, 620–628 (2007).
[Crossref]

2005 (2)

B. Stevenson, R. O’Connor, W. Kendall, A. Stocker, W. Schaff, R. Holasek, D. Even, D. Alexa, J. Salvador, M. Eismann, R. Mack, P. Kee, S. Harris, B. Karch, and J. Kershenstein, “The civil air patrol archer hyperspectral sensor system,” Proc. SPIE 5787, 17–28 (2005).
[Crossref]

C. Leonard, D. Michael, J. Gradie, J. Iokepa, and C. Stalder, “Performance of an EO/IR Sensor system in marine search and rescue,” Proc. SPIE 5787, 122–133 (2005).
[Crossref]

2003 (3)

J. Dowdall, I. Pavlidis, and G. Bebis, “Face detection in the near-IR spectrum,” Proc. SPIE 5074, 745–756 (2003).

Z. Pan, G. Healey, M. Prasad, and B. Tromberg, “Face recognition in hyperspectral images,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1552–1560 (2003).
[Crossref]

M. Storring, T. Kocka, H. J. Andersen, and E. Granum, “Tracking regions of human skin through illumination changes,” Pattern Recogn. Lett. 24, 1715–1723 (2003).
[Crossref]

2002 (5)

M. J. Jones and J. M. Rehg, “Statistical color models with application to skin detection,” Int. J. Comput. Vis. 46, 81–96 (2002).
[Crossref]

M. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 34–58 (2002).
[Crossref]

D. Manolakis and G. Shaw, “Detection algorithms for hyperspectral imaging applications,” IEEE Signal Process. Mag. 19(1), 29–43 (2002).
[Crossref]

M. Topping, J. Pfeiffer, A. Sparks, K. Jim, and D. Yoon, “Advanced airborn hyperspectral imaging system (AAHIS),” Proc. SPIE 4816, 1–11 (2002).
[Crossref]

C. Simi, A. Hill, and H. Kling, “Airborne remote spectrometry support to rescue personnel at Ground Zero after the World Trade Center attack on September 11, 2001,” Proc. SPIE 4816, 23–32 (2002).
[Crossref]

2001 (1)

E. Angelopoulou, “Understanding the color of human skin,” Hum. Vis. Electron. Imaging VI 4299, 243–251 (2001).

2000 (3)

I. Pavlidis, P. Symosek, B. Fritz, M. Bazakos, and N. Papanikolopoulos, “Automatic detection of vehicle occupants: the imaging problem and its solution,” Mach. Vis. Appl. 11, 313–320 (2000).

J.-R. Simard, P. Mathieu, G. Fournier, and V. Larochelle, “A range-gated intensified spectrographic imager: an instrument for active hyperspectral imaging,” Proc. SPIE 4035, 180–191 (2000).
[Crossref]

R. Sanchey-Reillo, C. Sanchez-Avila, and A. Gonzalez-Marcos, “Biometric identification through hand geometry measurements,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1168–1171 (2000).
[Crossref]

1998 (2)

S. Subramanian and N. Gat, “Subpixel object detection using hyperspectral imaging for search and rescue operations,” Proc. SPIE 3371, 216–225 (1998).
[Crossref]

H. Wang and S.-F. Chang, “Rapid modeling of diffuse reflectance of light in turbid slabs,” J. Opt. Soc. Am. 15, 936–944 (1998).
[Crossref]

1997 (1)

J. Daugman, “Face and gesture recognition: Overview,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 675–676 (1997).
[Crossref]

1996 (1)

T. Moon, “The expectation-maximization algorithm,” IEEE Signal Process. Mag. 13(6), 47–60 (1996).
[Crossref]

1995 (1)

N. Kollias, “The physical basis of skin color and its evaluation,” Clinics Dermatol. 13, 361–367 (1995).

1994 (1)

H. Buiteveld, J. Hakvoort, and M. Donze, “The optical properties of pure water,” Proc. SPIE 2258, 174–183 (1994).
[Crossref]

1990 (1)

F. Kruse, K. Kierein-Young, and J. Boardman, “Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83–92 (1990).

1981 (1)

R. Anderson and J. Parrish, “The optics of human skin,” J. Invest. Dermatol. 77, 13–19 (1981).
[Crossref]

1974 (1)

1965 (1)

S. Shapiro and M. Wilk, “An analysis of variance test for normality (complete samples),” Biometrika 52, 591–611 (1965).
[Crossref]

1945 (1)

F. Wilcoxon, “Individual comparisons by ranking methods,” Biom. Bull. 16, 80–83 (1945).

Abdel-Mottaleb, M.

R. Hsu, M. Abdel-Mottaleb, and A. K. Jain, “Face detection in color images,” in IEEE International Conference on Image Processing (ICIP) (2001), Vol. 1, pp. 1046–1049.

M. Abdel-Mottaleb and A. Elgammal, “Method for detecting a face in a digital image,” U.S. patent6,574,354 (June3, 2001).

Ahuja, N.

M. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 34–58 (2002).
[Crossref]

Albiol, A.

A. Albiol, L. Torres, and E. Delp, “Optimum color spaces for skin detection,” in IEEE International Conference on Image Processing (ICIP) (2001), Vol. 1, pp. 122–124.

Alexa, D.

B. Stevenson, R. O’Connor, W. Kendall, A. Stocker, W. Schaff, R. Holasek, D. Even, D. Alexa, J. Salvador, M. Eismann, R. Mack, P. Kee, S. Harris, B. Karch, and J. Kershenstein, “The civil air patrol archer hyperspectral sensor system,” Proc. SPIE 5787, 17–28 (2005).
[Crossref]

Andersen, H. J.

M. Storring, T. Kocka, H. J. Andersen, and E. Granum, “Tracking regions of human skin through illumination changes,” Pattern Recogn. Lett. 24, 1715–1723 (2003).
[Crossref]

Anderson, R.

R. Anderson and J. Parrish, “The optics of human skin,” J. Invest. Dermatol. 77, 13–19 (1981).
[Crossref]

Andreeva, A.

V. Vezhnevets, V. Sazonov, and A. Andreeva, “A survey on pixel-based skin color detection techniques,” in Proceedings of GraphiCon (2003), pp. 80–92.

Angelopoulou, E.

E. Angelopoulou, “Understanding the color of human skin,” Hum. Vis. Electron. Imaging VI 4299, 243–251 (2001).

Baranoski, G.

A. Krishnaswamy and G. Baranoski, “A study of skin optics,” (University of Waterloo, 2004).

Bazakos, M.

I. Pavlidis, P. Symosek, B. Fritz, M. Bazakos, and N. Papanikolopoulos, “Automatic detection of vehicle occupants: the imaging problem and its solution,” Mach. Vis. Appl. 11, 313–320 (2000).

Bebis, G.

J. Dowdall, I. Pavlidis, and G. Bebis, “Face detection in the near-IR spectrum,” Proc. SPIE 5074, 745–756 (2003).

Boardman, J.

F. Kruse, K. Kierein-Young, and J. Boardman, “Mineral mapping at Cuprite, Nevada with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83–92 (1990).

Brand, J.

J. Brand and J. Mason, “A comparative assessment of three approaches to pixel-level human skin-detection,” in Proceedings of 15th International Conference on Pattern Recognition (2000), Vol. 1, pp. 1056–1059.

Buiteveld, H.

H. Buiteveld, J. Hakvoort, and M. Donze, “The optical properties of pure water,” Proc. SPIE 2258, 174–183 (1994).
[Crossref]

Chang, S.-F.

H. Wang and S.-F. Chang, “Rapid modeling of diffuse reflectance of light in turbid slabs,” J. Opt. Soc. Am. 15, 936–944 (1998).
[Crossref]

Chekima, A.

C. Doukim, J. Dargham, A. Chekima, and S. Omatu, “Combining neural networks for skin detection,” Signal Image Process. 1, 1–11 (2010).

Clark, R.

R. Clark, G. Swayze, R. Wise, E. Livo, T. Hoefen, R. Kokaly, and S. Sutley, USGS Digital Spectral Library Splib06a: U.S. Geological Survey, Digital Data Series 231, 2007. Online http://speclab.cr.usgs.gov/spectral.lib06.

Dargham, J.

C. Doukim, J. Dargham, A. Chekima, and S. Omatu, “Combining neural networks for skin detection,” Signal Image Process. 1, 1–11 (2010).

Daugman, J.

J. Daugman, “Face and gesture recognition: Overview,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 675–676 (1997).
[Crossref]

Deering, D. W.

J. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” in Third Earth Resources Technology Satellite-1 Symposium (1973), pp. 309–317.

Delp, E.

A. Albiol, L. Torres, and E. Delp, “Optimum color spaces for skin detection,” in IEEE International Conference on Image Processing (ICIP) (2001), Vol. 1, pp. 122–124.

Donze, M.

H. Buiteveld, J. Hakvoort, and M. Donze, “The optical properties of pure water,” Proc. SPIE 2258, 174–183 (1994).
[Crossref]

Doukim, C.

C. Doukim, J. Dargham, A. Chekima, and S. Omatu, “Combining neural networks for skin detection,” Signal Image Process. 1, 1–11 (2010).

Dowdall, J.

J. Dowdall, I. Pavlidis, and G. Bebis, “Face detection in the near-IR spectrum,” Proc. SPIE 5074, 745–756 (2003).

Dykes, P.

P. Matts, P. Dykes, and R. Marks, “The distribution of melanin in skin determined in vivo,” Br. J. Dermatol. 156, 620–628 (2007).
[Crossref]

Eismann, M.

B. Stevenson, R. O’Connor, W. Kendall, A. Stocker, W. Schaff, R. Holasek, D. Even, D. Alexa, J. Salvador, M. Eismann, R. Mack, P. Kee, S. Harris, B. Karch, and J. Kershenstein, “The civil air patrol archer hyperspectral sensor system,” Proc. SPIE 5787, 17–28 (2005).
[Crossref]

Elgammal, A.

M. Abdel-Mottaleb and A. Elgammal, “Method for detecting a face in a digital image,” U.S. patent6,574,354 (June3, 2001).

Even, D.

B. Stevenson, R. O’Connor, W. Kendall, A. Stocker, W. Schaff, R. Holasek, D. Even, D. Alexa, J. Salvador, M. Eismann, R. Mack, P. Kee, S. Harris, B. Karch, and J. Kershenstein, “The civil air patrol archer hyperspectral sensor system,” Proc. SPIE 5787, 17–28 (2005).
[Crossref]

Fleck, M. M.

M. M. Fleck, “Finding naked people,” in Proceedings of the 4th European Conference on Computer Vision (1996), Vol. 1065, pp. 593–602.

Fournier, G.

J.-R. Simard, P. Mathieu, G. Fournier, and V. Larochelle, “A range-gated intensified spectrographic imager: an instrument for active hyperspectral imaging,” Proc. SPIE 4035, 180–191 (2000).
[Crossref]

Friedman, J.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, 2001).

Fritz, B.

I. Pavlidis, P. Symosek, B. Fritz, M. Bazakos, and N. Papanikolopoulos, “Automatic detection of vehicle occupants: the imaging problem and its solution,” Mach. Vis. Appl. 11, 313–320 (2000).

Gat, N.

S. Subramanian and N. Gat, “Subpixel object detection using hyperspectral imaging for search and rescue operations,” Proc. SPIE 3371, 216–225 (1998).
[Crossref]

Gomez, G.

G. Gomez, “On selecting colour components for skin detection,” in Proceedings of International Conference on Pattern Recognition (2002), Vol. 2, pp. 961–964.

Gonzalez-Marcos, A.

R. Sanchey-Reillo, C. Sanchez-Avila, and A. Gonzalez-Marcos, “Biometric identification through hand geometry measurements,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 1168–1171 (2000).
[Crossref]

Gradie, J.

C. Leonard, D. Michael, J. Gradie, J. Iokepa, and C. Stalder, “Performance of an EO/IR Sensor system in marine search and rescue,” Proc. SPIE 5787, 122–133 (2005).
[Crossref]

Granum, E.

M. Storring, T. Kocka, H. J. Andersen, and E. Granum, “Tracking regions of human skin through illumination changes,” Pattern Recogn. Lett. 24, 1715–1723 (2003).
[Crossref]

Haas, R. H.

J. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, “Monitoring vegetation systems in the Great Plains with ERTS,” in Third Earth Resources Technology Satellite-1 Symposium (1973), pp. 309–317.

Hakvoort, J.

H. Buiteveld, J. Hakvoort, and M. Donze, “The optical properties of pure water,” Proc. SPIE 2258, 174–183 (1994).
[Crossref]

Hanbury, A.

R. Khan, A. Hanbury, and J. Stoettinger, “Skin detection: A random forest approach,” in 17th IEEE International Conference on Image Processing (ICIP) (2010), pp. 4613–4616.

Harris, S.

B. Stevenson, R. O’Connor, W. Kendall, A. Stocker, W. Schaff, R. Holasek, D. Even, D. Alexa, J. Salvador, M. Eismann, R. Mack, P. Kee, S. Harris, B. Karch, and J. Kershenstein, “The civil air patrol archer hyperspectral sensor system,” Proc. SPIE 5787, 17–28 (2005).
[Crossref]

Hastie, T.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning (Springer, 2001).

Healey, G.

Z. Pan, G. Healey, M. Prasad, and B. Tromberg, “Face recognition in hyperspectral images,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1552–1560 (2003).
[Crossref]

Hill, A.

C. Simi, A. Hill, and H. Kling, “Airborne remote spectrometry support to rescue personnel at Ground Zero after the World Trade Center attack on September 11, 2001,” Proc. SPIE 4816, 23–32 (2002).
[Crossref]

Hoefen, T.

R. Clark, G. Swayze, R. Wise, E. Livo, T. Hoefen, R. Kokaly, and S. Sutley, USGS Digital Spectral Library Splib06a: U.S. Geological Survey, Digital Data Series 231, 2007. Online http://speclab.cr.usgs.gov/spectral.lib06.

Holasek, R.

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Z. Pan, G. Healey, M. Prasad, and B. Tromberg, “Face recognition in hyperspectral images,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1552–1560 (2003).
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M. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 34–58 (2002).
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Pattern Recogn. Lett. (2)

J. C. SanMiguel and S. Suja, “Skin detection by dual maximization of detectors agreement for video monitoring,” Pattern Recogn. Lett. 34, 2102–2109 (2013).
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Figures (11)

Fig. 1.
Fig. 1.

Spectra of Type I/II, Type III/IV, and Type V/VI skin. Spectral measurements were acquired by the authors using analytical spectral devices (ASD) field spectroradiometer (FieldSpec3).

Fig. 2.
Fig. 2.

(Left y -axis) Absorption coefficients of melanin [32], oxygenated hemoglobin [33], and water [34,35]. (Right y -axis) Scattering coefficient of skin from [36,37].

Fig. 3.
Fig. 3.

Spectra of Type I/II skin, Type III/IV skin, plastic doll, and brown cardboard. Spectral measurements were acquired by the authors using an ASD FieldSpec3.

Fig. 4.
Fig. 4.

Solar irradiance scaled by the maximum irradiance and the radiance spectra of Type I/II skin (from Fig. 1) illuminated by sunlight scaled by the same maximum irradiance. Data were collected by the authors on a sunny day in Dayton, OH (USA) using an ASD FieldSpec3 and a dome style-reflective cosine receptor for measuring full-sky irradiance.

Fig. 5.
Fig. 5.

Eight test images acquired in a suburban environment at different stand-off distances: (a) 3 m, (b) 20 m (subjects 7–9 are at 40 m visible on the left), (c) 3 m, and (d) 30 m, (e) 3 m (f) 7 m (g) 3 m, and (h) 3 m. Subjects (1,2,4,6–9,15,16) are Type I/II skin; subjects (3,5,13,14) are Type III/IV skin; and subjects (10–12) are Type V/VI skin. (a) Subjects 1–14 are labeled, (b) subject 15, and (g) subject 16. False-alarm sources are include (left-to-right): a branch from a conifer, flesh-colored shirt, color photograph of a person, wood, brown leather boot, cardboard, red brick, stick lying horizontally, flesh-colored doll, and a leather glove all sitting on top of a gray plastic container. Spectralon calibration panels appear below the brown leather boot (partially visible, dark gray) and below the flesh-colored doll (white). Image was acquired by the authors using the SpecTIR HyperSpecTIR Version 3 (HST3) Hyperspectral Imager [48]. All faces are blurred in all images to protect the identity of the volunteers.

Fig. 6.
Fig. 6.

Display of the (NDGRI, NDSI) feature space computed from the HST3 Hyperspectral Image shown in Fig. 5(b). Black dots are skin features and gray dots are background features.

Fig. 7.
Fig. 7.

Response curves for wavelength dependent conversion factors used to transform hyperspectral spectra to CIE XYZ form [50].

Fig. 8.
Fig. 8.

Display of the ( C b , C r ) feature space computed from the RGB conversion of the HST3 hyperspectral image shown in Fig. 5(b). Black dots are skin features and gray dots are background features.

Fig. 9.
Fig. 9.

Display of the ( H , S ) feature space computed from the RGB conversion of the HST3 hyperspectral image shown in Fig. 5(b). Black dots are skin features and gray dots are background features.

Fig. 10.
Fig. 10.

Skin detection ROC curves averaged over K = 10 cross validation trials.

Fig. 11.
Fig. 11.

(a) Detection results for the test seen, evaluated at the thresholds used to determine the EERs listed in Table 5 for (b)  ( H , S ) features, (c)  ( C b , C r ) features, and (d) (NDGRI, NDSI) features. Skin detections are based on an agreement with six of the ten models and are shown as white pixels in (b)–(d). Black pixels are background pixels.

Tables (6)

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Table 1. Fitzpatrick Six-Level Characterization of Human Skin Based on Its Likelihood of Burning [30]

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Table 2. Hyperspectral Image Dimensions for Each Image in Fig. 5

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Table 3. Hyperspectral Image Bands Used to Generate the (NDGRI, NDSI) Feature Pair

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Table 4. Number of Gaussian Mixtures used for the LRT

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Table 5. Equal Error Rate Detection and False Alarm Values and Statistics for Each Set of the Three Detection-Features Conducted over K = 10 Trials a

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Table 6. Area under the Receiver Operating Characteristic Curve and Statistics for Each Set of the Three Detection-Features Conducted over K = 10 Trials a

Equations (12)

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γ i = ρ ^ λ 1 = 1080 nm i ρ ^ λ 2 = 1580 nm i ρ ^ λ 1 = 1080 nm i + ρ ^ λ 2 = 1580 nm i ,
β i = ρ ^ λ 1 = 540 nm i ρ ^ λ 2 = 660 nm i ρ ^ λ 1 = 540 nm i + ρ ^ λ 2 = 660 nm i ,
Λ Θ ( θ ) = f ^ 1 ( θ ) f ^ 0 ( θ ) H 0 H 1 η ,
f ^ 0 ( θ ) = P [ Θ = θ | not skin ] , f ^ 1 ( θ ) = P [ Θ = θ | skin ] , Θ = { B , Γ } , θ = { β , γ } ,
f ^ j ( θ ) = k = 1 K j π j , k N ( μ ̲ j , k , Σ ̲ j , k ) , j { 0 , 1 } ,
ρ ^ λ k i = G λ k L λ k i + O λ k ,
G λ k = ρ B , λ k ρ D , λ k L B , λ k L D , λ k ,
O λ k = L B , λ k ρ D , λ k L D , λ k ρ B , λ k L B , λ k L D , λ k .
[ R G B ] = [ 2.3710 0.9000 0.4710 0.5140 1.4250 0.0890 0.0005 0.0150 1.0090 ] [ X Y Z ] .
Y = 0.299 R + 0.587 G + 0.114 B , C b = B Y , C r = R Y .
H = arccos 1 2 ( ( R G ) + ( R B ) ) ( ( R G ) 2 + ( R B ) ( G B ) ) , S = 1 3 × min { R , G , B } R + G + B , V = 1 3 ( R + G + B ) ,
P FA = P Miss , P FA = ( 1 P D ) , P FA + P D = 1 .

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