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Comparative Study of Statistical Skin Detection Algorithms for
Sub-Continental Human Images
release_ukuydbhwpbhx3hzfi3ophevjj4
by
Mirza Rehenuma Tabassum,
Alim Ul Gias,
Md. Mostafa Kamal,
Hossain
Muhammad Muctadir,
Muhammad Ibrahim,
Asif Khan Shakir,
Asif Imran,
Saiful
Islamm,
Md. Golam Rabbani,
Shah Mostafa Khaled,
Md. Saiful Islam,
Zerina
Begum
Released
as a article
.
2010
Abstract
Object detection has been a focus of research in human-computer interaction.
Skin area detection has been a key to different recognitions like face
recognition, human motion detection, pornographic and nude image prediction,
etc. Most of the research done in the fields of skin detection has been trained
and tested on human images of African, Mongolian and Anglo-Saxon ethnic
origins. Although there are several intensity invariant approaches to skin
detection, the skin color of Indian sub-continentals have not been focused
separately. The approach of this research is to make a comparative study
between three image segmentation approaches using Indian sub-continental human
images, to optimize the detection criteria, and to find some efficient
parameters to detect the skin area from these images. The experiments observed
that HSV color model based approach to Indian sub-continental skin detection is
more suitable with considerable success rate of 91.1% true positives and 88.1%
true negatives.
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arXiv
1008.4206v1
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