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Contribution of Skin Color Cue in Face Detection Applications

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Advances in Low-Level Color Image Processing

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 11))

Abstract

Face detection has been considered as one of the most active areas of research due to its wide range of applications in computer vision and digital image processing technology. In order to build a robust face detection system, several cues, such as motion, shape, color, and texture have been considered. Among available cues, color is one of the most effective ones due to its computational efficiency, high discriminative power, as well as robustness against geometrical transform. This chapter investigates the role of skin color cue in automatic face detection systems. General overview of existing face detection techniques and skin pixel classification solutions are provided. Further, illumination adaptation strategies for skin color detection are discussed to overcome the sensitivity of skin color analysis against illumination variation. Finally, two case studies are presented to provide more realistic view of contribution of skin color cue in face detection frameworks.

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Notes

  1. 1.

    The term real-time implies the capability to process image frames with a rate close to the examined sequence frame rate. In [67], real-time requirement is defined to be approximately 15 frames per second for 384x288 image.

  2. 2.

    Under the white illumination condition with Lambertian reflection assumption, normalized RGB is invariant to illumination direction and illumination intensity [21]

  3. 3.

    Linear RGB implies that it is linear to the physical intensity, whereas nonlinear RGB is non-linear to intensity. Such nonlinearity is introduced to RGB signal by gamma correction process in order to compensate a nonlinear response of CRT display devices.

  4. 4.

    It is noteworthy to mention that the original literature [57] does not clearly indicate whether linear or nonlinear RGB is used in conversion. Although there is such an ambiguity, we use nonlinear RGB in this paper, which is implicit in image processing applications [52].

  5. 5.

    For image reproduction applications, the canonical illuminant is often defined as an illuminant for which the camera sensor is balanced [2].

  6. 6.

    Lambertian reflection model explains the relationship between the surface reflectance and color image formation for flat, matte surfaces. Although this model does not hold true for all materials, it provides a good approximation in general, and thus widely used in design of tractable color constancy solutions

  7. 7.

    A surface with perfect reflectance property reflects the incoming light in the entire visible spectral range (between wavelengths of about 400 and 700 nm of the electromagnetic spectrum)

  8. 8.

    Assigning more mixture components for non-skin class than skin class is beneficial due to less compact shape of non-skin sample distribution. However, we found that performance gain from having more components for non-skin class is marginal and thus we maintain the same number of components for both classes in this experiment.

  9. 9.

    Viola and Jones [67] indicate that around \(1\times 10^{-6}\) of FPR is a common value for practical uses. However, it is extremely difficult to achieve the precise value and generally it is acceptable if FPR is within the same magnitude. For instance, Jun and Kim [37] achieves \(96\,\%\) TPR at \(2.56\times 10^{-6}\) FPR, and Louis and Plataniotis [47] achieves \(92.27\,\%\) TPR at \(6.2\times 10^{-6}\) FPR

  10. 10.

    Instead of measuring average number of scanned window and execution time per frame, we measured the sum of them, since test images in Bao database vary in spatial resolutions

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Authors and Affiliations

  1. Multimedia Lab, The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Canada

    Dohyoung Lee, Jeaff Wang & Konstantinos N. Plataniotis

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  1. Dohyoung Lee
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  2. Jeaff Wang
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  3. Konstantinos N. Plataniotis
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Correspondence to Dohyoung Lee .

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Editors and Affiliations

  1. Computer Science dept., Louisiana State University, Shreveport, Louisiana, USA

    M. Emre Celebi

  2. Automatic Control, Silesian University of Technology, Gliwice, Poland

    Bogdan Smolka

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Lee, D., Wang, J., Plataniotis, K.N. (2014). Contribution of Skin Color Cue in Face Detection Applications. In: Celebi, M., Smolka, B. (eds) Advances in Low-Level Color Image Processing. Lecture Notes in Computational Vision and Biomechanics, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7584-8_12

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