Abstract

We previously proposed a filter that could detect cosmetic foundations with high discrimination accuracy [Opt. Express 19, 6020 (2011)]. This study extends the filter’s functionality to the quantification of the amount of foundation and applies the filter for the assessment of spatial distributions of foundation under realistic facial conditions. Human faces that are applied with quantitatively controlled amounts of cosmetic foundations were measured using the filter. A calibration curve between pixel values of the image and the amount of foundation was created. The optical filter was applied to visualize spatial foundation distributions under realistic facial conditions, which clearly indicated areas on the face where foundation remained even after cleansing. Results confirm that the proposed filter could visualize and nondestructively inspect the foundation distributions.

© 2011 OSA

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References

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  1. K. Nishino, M. Nakamura, M. Matsumoto, O. Tanno, and S. Nakauchi, “Optical filter for highlighting spectral features Part I: design and development of the filter for discrimination of human skin with and without an application of cosmetic foundation,” Opt. Express 19(7), 6020–6030 (2011).
    [CrossRef] [PubMed]
  2. E. Angelopoulou, The reflectance spectrum of human skin, (Technical Report MS-CIS-99–29, GRASP Laboratory, Department of Computer and Information Science, University of Pennsylvania, USA, 1999).
  3. N. Tsumura, M. Kawabuchi, H. Haneishi, and Y. Miyake, “Mapping pigmentation in human skin by multi-visible-spectral imaging by inverse optical scattering technique,” J. Imag. Sci. Tech. 45(5), 444–450 (2001).
  4. I. V. Meglinski and S. J. Matcher, “Quantitative assessment of skin layers absorption and skin reflectance spectra simulation in the visible and near-infrared spectral regions,” Physiol. Meas. 23(4), 741–753 (2002).
    [CrossRef] [PubMed]
  5. G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ,” Pigment Cell Res. 17(6), 619–626 (2004).
    [CrossRef]
  6. M. Moncrieff, S. Cotton, E. Claridge, and P. Hall, “Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions,” Br. J. Dermatol. 146(3), 448–457 (2002).
    [CrossRef] [PubMed]
  7. J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
    [CrossRef] [PubMed]
  8. G. N. Stamatas, M. Southall, and N. Kollias, “In vivo monitoring of cutaneous edema using spectral imaging in the visible and near infrared,” J. Invest. Dermatol. 126(8), 1753–1760 (2006).
    [CrossRef] [PubMed]
  9. G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt. 12(5), 051603 (2007).
    [CrossRef] [PubMed]
  10. M. Doi, R. Ohtsuki, and S. Tominaga, “Spectral estimation of made-up skin color under various conditions,” Proc. SPIE (San Jose, California, USA), pp. 606204 (2006).
  11. S. J. Preece and E. Claridge, “Spectral filter optimization for the recovery of parameters which describe human skin,” IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 913–922 (2004).
    [CrossRef]
  12. G. Ardeshir, “Image Registration by approximation method,” Image Vis. Comput. 6(4), 255–261 (1988).
    [CrossRef]
  13. P. Kubelka, “New contributions to the optics of intensely light-scattering materials. Part I,” J. Opt. Soc. Am. 38(5), 448–457 (1948).
    [CrossRef] [PubMed]

2011 (1)

2007 (1)

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt. 12(5), 051603 (2007).
[CrossRef] [PubMed]

2006 (1)

G. N. Stamatas, M. Southall, and N. Kollias, “In vivo monitoring of cutaneous edema using spectral imaging in the visible and near infrared,” J. Invest. Dermatol. 126(8), 1753–1760 (2006).
[CrossRef] [PubMed]

2004 (2)

S. J. Preece and E. Claridge, “Spectral filter optimization for the recovery of parameters which describe human skin,” IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 913–922 (2004).
[CrossRef]

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ,” Pigment Cell Res. 17(6), 619–626 (2004).
[CrossRef]

2002 (3)

M. Moncrieff, S. Cotton, E. Claridge, and P. Hall, “Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions,” Br. J. Dermatol. 146(3), 448–457 (2002).
[CrossRef] [PubMed]

J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
[CrossRef] [PubMed]

I. V. Meglinski and S. J. Matcher, “Quantitative assessment of skin layers absorption and skin reflectance spectra simulation in the visible and near-infrared spectral regions,” Physiol. Meas. 23(4), 741–753 (2002).
[CrossRef] [PubMed]

2001 (1)

N. Tsumura, M. Kawabuchi, H. Haneishi, and Y. Miyake, “Mapping pigmentation in human skin by multi-visible-spectral imaging by inverse optical scattering technique,” J. Imag. Sci. Tech. 45(5), 444–450 (2001).

1988 (1)

G. Ardeshir, “Image Registration by approximation method,” Image Vis. Comput. 6(4), 255–261 (1988).
[CrossRef]

1948 (1)

Ardeshir, G.

G. Ardeshir, “Image Registration by approximation method,” Image Vis. Comput. 6(4), 255–261 (1988).
[CrossRef]

Beer, J. Z.

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ,” Pigment Cell Res. 17(6), 619–626 (2004).
[CrossRef]

Claridge, E.

S. J. Preece and E. Claridge, “Spectral filter optimization for the recovery of parameters which describe human skin,” IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 913–922 (2004).
[CrossRef]

M. Moncrieff, S. Cotton, E. Claridge, and P. Hall, “Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions,” Br. J. Dermatol. 146(3), 448–457 (2002).
[CrossRef] [PubMed]

Cotton, S.

M. Moncrieff, S. Cotton, E. Claridge, and P. Hall, “Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions,” Br. J. Dermatol. 146(3), 448–457 (2002).
[CrossRef] [PubMed]

Hall, P.

M. Moncrieff, S. Cotton, E. Claridge, and P. Hall, “Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions,” Br. J. Dermatol. 146(3), 448–457 (2002).
[CrossRef] [PubMed]

Haneishi, H.

N. Tsumura, M. Kawabuchi, H. Haneishi, and Y. Miyake, “Mapping pigmentation in human skin by multi-visible-spectral imaging by inverse optical scattering technique,” J. Imag. Sci. Tech. 45(5), 444–450 (2001).

Jovel, C.

J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
[CrossRef] [PubMed]

Kawabuchi, M.

N. Tsumura, M. Kawabuchi, H. Haneishi, and Y. Miyake, “Mapping pigmentation in human skin by multi-visible-spectral imaging by inverse optical scattering technique,” J. Imag. Sci. Tech. 45(5), 444–450 (2001).

Kollias, N.

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt. 12(5), 051603 (2007).
[CrossRef] [PubMed]

G. N. Stamatas, M. Southall, and N. Kollias, “In vivo monitoring of cutaneous edema using spectral imaging in the visible and near infrared,” J. Invest. Dermatol. 126(8), 1753–1760 (2006).
[CrossRef] [PubMed]

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ,” Pigment Cell Res. 17(6), 619–626 (2004).
[CrossRef]

Kubelka, P.

Matcher, S. J.

I. V. Meglinski and S. J. Matcher, “Quantitative assessment of skin layers absorption and skin reflectance spectra simulation in the visible and near-infrared spectral regions,” Physiol. Meas. 23(4), 741–753 (2002).
[CrossRef] [PubMed]

Matsumoto, M.

Meglinski, I. V.

I. V. Meglinski and S. J. Matcher, “Quantitative assessment of skin layers absorption and skin reflectance spectra simulation in the visible and near-infrared spectral regions,” Physiol. Meas. 23(4), 741–753 (2002).
[CrossRef] [PubMed]

Miyake, Y.

N. Tsumura, M. Kawabuchi, H. Haneishi, and Y. Miyake, “Mapping pigmentation in human skin by multi-visible-spectral imaging by inverse optical scattering technique,” J. Imag. Sci. Tech. 45(5), 444–450 (2001).

Moncrieff, M.

M. Moncrieff, S. Cotton, E. Claridge, and P. Hall, “Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions,” Br. J. Dermatol. 146(3), 448–457 (2002).
[CrossRef] [PubMed]

Nakamura, M.

Nakauchi, S.

Nishino, K.

Norton, H. L.

J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
[CrossRef] [PubMed]

Parra, E. J.

J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
[CrossRef] [PubMed]

Preece, S. J.

S. J. Preece and E. Claridge, “Spectral filter optimization for the recovery of parameters which describe human skin,” IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 913–922 (2004).
[CrossRef]

Shriver, M. D.

J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
[CrossRef] [PubMed]

Southall, M.

G. N. Stamatas, M. Southall, and N. Kollias, “In vivo monitoring of cutaneous edema using spectral imaging in the visible and near infrared,” J. Invest. Dermatol. 126(8), 1753–1760 (2006).
[CrossRef] [PubMed]

Stamatas, G. N.

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt. 12(5), 051603 (2007).
[CrossRef] [PubMed]

G. N. Stamatas, M. Southall, and N. Kollias, “In vivo monitoring of cutaneous edema using spectral imaging in the visible and near infrared,” J. Invest. Dermatol. 126(8), 1753–1760 (2006).
[CrossRef] [PubMed]

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ,” Pigment Cell Res. 17(6), 619–626 (2004).
[CrossRef]

Tanno, O.

Tsumura, N.

N. Tsumura, M. Kawabuchi, H. Haneishi, and Y. Miyake, “Mapping pigmentation in human skin by multi-visible-spectral imaging by inverse optical scattering technique,” J. Imag. Sci. Tech. 45(5), 444–450 (2001).

Wagner, J. K.

J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
[CrossRef] [PubMed]

Zmudzka, B. Z.

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ,” Pigment Cell Res. 17(6), 619–626 (2004).
[CrossRef]

Br. J. Dermatol. (1)

M. Moncrieff, S. Cotton, E. Claridge, and P. Hall, “Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions,” Br. J. Dermatol. 146(3), 448–457 (2002).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

S. J. Preece and E. Claridge, “Spectral filter optimization for the recovery of parameters which describe human skin,” IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 913–922 (2004).
[CrossRef]

Image Vis. Comput. (1)

G. Ardeshir, “Image Registration by approximation method,” Image Vis. Comput. 6(4), 255–261 (1988).
[CrossRef]

J. Biomed. Opt. (1)

G. N. Stamatas and N. Kollias, “In vivo documentation of cutaneous inflammation using spectral imaging,” J. Biomed. Opt. 12(5), 051603 (2007).
[CrossRef] [PubMed]

J. Imag. Sci. Tech. (1)

N. Tsumura, M. Kawabuchi, H. Haneishi, and Y. Miyake, “Mapping pigmentation in human skin by multi-visible-spectral imaging by inverse optical scattering technique,” J. Imag. Sci. Tech. 45(5), 444–450 (2001).

J. Invest. Dermatol. (1)

G. N. Stamatas, M. Southall, and N. Kollias, “In vivo monitoring of cutaneous edema using spectral imaging in the visible and near infrared,” J. Invest. Dermatol. 126(8), 1753–1760 (2006).
[CrossRef] [PubMed]

J. Opt. Soc. Am. (1)

Opt. Express (1)

Physiol. Meas. (1)

I. V. Meglinski and S. J. Matcher, “Quantitative assessment of skin layers absorption and skin reflectance spectra simulation in the visible and near-infrared spectral regions,” Physiol. Meas. 23(4), 741–753 (2002).
[CrossRef] [PubMed]

Pigment Cell Res. (2)

G. N. Stamatas, B. Z. Zmudzka, N. Kollias, and J. Z. Beer, “Non-invasive measurements of skin pigmentation in situ,” Pigment Cell Res. 17(6), 619–626 (2004).
[CrossRef]

J. K. Wagner, C. Jovel, H. L. Norton, E. J. Parra, and M. D. Shriver, “Comparing quantitative measures of erythema, pigmentation and skin response using reflectometry,” Pigment Cell Res. 15(5), 379–384 (2002).
[CrossRef] [PubMed]

Other (2)

E. Angelopoulou, The reflectance spectrum of human skin, (Technical Report MS-CIS-99–29, GRASP Laboratory, Department of Computer and Information Science, University of Pennsylvania, USA, 1999).

M. Doi, R. Ohtsuki, and S. Tominaga, “Spectral estimation of made-up skin color under various conditions,” Proc. SPIE (San Jose, California, USA), pp. 606204 (2006).

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Figures (9)

Fig. 1
Fig. 1

(a) Spectral transmittance of the theoretically designed and the optically realized filter. Theoretical transmittance was designed by optimization. The optical filter was realized by the vacuum deposition technology. (b) Developed optical filter. The optical filter was made of a multilayer thin film that was composed of 31 layers of SiO2 and TiO2.

Fig. 2
Fig. 2

Areas of application of cosmetic foundation for quantitative measurement. Cosmetic foundation was applied to 14 areas. Observation angles were −45, 0, and 45°. The application areas indicated in each facial image were used for the following analysis.

Fig. 3
Fig. 3

(a) Discriminant scores that were manually extracted from the measured images. The error bars show the standard deviations. (b) Estimation errors due to the observation angle. Solid lines show the discriminant scores of the forehead and jaw (Nos. 1–4, 13, and 14 shown in Fig. 2) observed at a 0° angle. Scores at the same positions observed at −45° and +45° are shown as broken lines.

Fig. 4
Fig. 4

Relationship between the applied and estimated amounts of foundation. Error bars denote the standard deviations. (a) Without baseline correction: decision coefficient is 0.9152 and SEP is 0.1557. (b) With baseline correction: decision coefficient is 0.8978 and SEP is 0.1645. Estimation accuracy is higher for (a) than that for (b). However, (b) has no error when the applied foundation is zero.

Fig. 5
Fig. 5

Comparison of foundation maps computed using different calibration curves. (a)Without baseline correction. (b) With baseline correction. These are results of one subject. Computed images were obtained from a make-up doll image by using the “local weighted mean method” of image transformation [12].

Fig. 6
Fig. 6

Foundation maps of test data showing the foundation distribution of realistic made-up skin. Cosmetic foundation was uniformly applied over the face so that the facial skin color looks uniform.

Fig. 7
Fig. 7

Average cosmetic foundation maps. (a) Foundation distribution of CS and (b) foundation distribution of CP. Average cosmetic foundation maps were computed using image transformation. Standard deviations among subjects were also computed for each condition and were used to show the reliability by changing the transparency rate depending on the standard deviation. The calibration curve with baseline correction (Eq. (6)) was used to compute the foundation map. All pixel values of (b) were zero because the average map of CP was used as the baseline image.

Fig. 8
Fig. 8

Comparison of the normal and high-water-resistant foundation. Figures show the average foundation distribution maps of CS. (a) The average map of subjects who used normal foundation and (b) the average map for high-water-resistant foundation.

Fig. 9
Fig. 9

Comparison of the Kubelka–Munk theory and the calibration curve. (a) Relationship between the relative thickness of the cosmetic foundation layer and the estimated spectral reflectance based on the Kubelka–Munk theory. This computation was performed under the assumption based on actual measured values that the parameters were Rinf = 0.6, Rg = 0, R0 = Rinf /100, and D0 = 1. Spectral reflectance of bare skin Rskin was changed from 0.2 to 0.35 in steps of 0.01. (b) Relationship between the estimated amount of liquid foundation and the discriminant score that was computed based on the calibration curve with baseline correction. Discriminant scores in bare skin were changed from −7 to 2 in steps of 0.5.

Tables (1)

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Table 1 Parameters and Evaluated Values of the Calibration Curve

Equations (8)

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C = ( r , g ) = ( C R C R + C G + C B , C G C R + C G + C B ) ,
f d ( C ) = ( Σ 1 ( μ 1 μ 2 ) ) t ( C μ 1 + μ 2 2 ) log ( p 2 / p 1 ) ,
y = a ln ( x + c ) + b ,
y = a { ln ( x + b y 0 + c ) ln ( b y 0 + c ) } + y 0 .
x = exp { y b a } c ,
x = ( b y 0 + c ) exp { y y 0 a } ( b y 0 + c ) .
R ( λ ) = ( 1 R S ) ( R m ( λ ) + T m ( λ ) R s k i n ( λ ) 1 R m ( λ ) R s k i n ( λ ) ) + R S , R m ( λ ) = 1 a m ( λ ) + b m ( λ ) cot h D m b m ( λ ) S m ( λ ) , T m ( λ ) = b m ( λ ) a m ( λ ) sin h D m b m ( λ ) S m ( λ ) + b m ( λ ) cos h D m b m ( λ ) S m ( λ ) ,
S m ( λ ) = 1 b m ( λ ) D 0 ( cot   h 1 a m ( λ ) R 0 ( λ ) b m ( λ ) cot   h 1 a m ( λ ) R g ( λ ) b m ( λ ) ) , a m = 1 2 ( 1 R + R ) , b m = ( a m 2 1 ) , 1 2

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