Integrated face detection approach for far image application
Face detection has been widely explored over the past few decades. Despite the significant progress in detecting human faces in unconstrained and complex images, face detection remains a challenging problem in computer vision, especially for the images captured at a far distance making it difficu...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/67088/1/FK%202016%20130%20IR.pdf |
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| Summary: | Face detection has been widely explored over the past few decades. Despite the significant
progress in detecting human faces in unconstrained and complex images, face
detection remains a challenging problem in computer vision, especially for the images
captured at a far distance making it difficult to detect face region. Other factors affecting
face detection are illumination conditions, pose and ethnicity. Therefore, the need
of a robust and efficient face detection algorithm is required to tackle these problems.
This thesis presents an Integrated face detection approach for far image application,
which solves the problems mentioned. The proposed approach consists of an Illumination
compensation method, a Skin segmentation method, a Noise reduction method
and Euler method. In the proposed illumination compensation method, the R, G and B
components were normalized using Gray World Theory (GWT), a theory that compensates
the illumination effect. The skin segmentation method consists of a combination
of RGB filter, the newly proposed filter known as a Dynamic chrominance filter and an
edge detector. The function of the RGB filter is to reject pixels with the RGB colors
that are most probably non-skin, so that the computation in the following stages does
not apply to all pixels. In this method, the final decision of a pixel belongs to the class
“skin is made by the Dynamic chrominance filter and the edge detector. The noise
reduction method in the proposed algorithm consists of a combination of a morphological
filter and a rejection method. The last stage of the algorithm is to apply the Euler
method, in which its function is to search for the facial features. The features indicate
whether the detected skin region is a region that represents face or non-face. Also,
an experiment was conducted on the developed database known as Large Variability
Surveillance Camera Face (LVSC) database and FEI database, the proposed method
produced a detection rate of 98.4% and 100%, respectively. |
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