A. Ritu Verma, and S. Sharma. ACEEE International Journal on Signal & Image Processing, 1 (3):
6(December 2010)
Abstract
Human face detection is a significant problem of
image processing and is usually a first step for face
recognition and visual surveillance. This paper presents the
details of face detection approach that is implemented to
achieve accurate face detection in group color images which
are based on facial feature and Support Vector Machine. In
the first step, the proposed approach quickly separates skin
color regions from the background and from non-skin color
regions using YCbCr color space transformation. After the
detection of skin regions, the images are processed with,
wavelet transforms (WT) and discrete cosine transforms
(DCT) as a result of which the 30×30 pixel sub images are
found. These sub images are then assigned to SVM classifier
as an input. The SVM is used to classify non-face regions from
the remaining regions more accurately, that are obtained
from previous steps and having big difference between faces
regions and non-faces regions. The experimental results on
different types of group color images show that this approach
improves the detection speed and minimizes the false
detection rate in less time and detects faces in different color
images.
%0 Journal Article
%1 rituverma2010robust
%A Ritu Verma, Anupam Agrawal
%A Sharma, Shanu
%D 2010
%E Das, Dr. Vinu V
%J ACEEE International Journal on Signal & Image Processing
%K Face_Detection Wavelet_Transform
%N 3
%P 6
%T A Robust & Fast Face Detection System
%U http://doi.searchdl.org/01.IJSIP.1.3.135
%V 1
%X Human face detection is a significant problem of
image processing and is usually a first step for face
recognition and visual surveillance. This paper presents the
details of face detection approach that is implemented to
achieve accurate face detection in group color images which
are based on facial feature and Support Vector Machine. In
the first step, the proposed approach quickly separates skin
color regions from the background and from non-skin color
regions using YCbCr color space transformation. After the
detection of skin regions, the images are processed with,
wavelet transforms (WT) and discrete cosine transforms
(DCT) as a result of which the 30×30 pixel sub images are
found. These sub images are then assigned to SVM classifier
as an input. The SVM is used to classify non-face regions from
the remaining regions more accurately, that are obtained
from previous steps and having big difference between faces
regions and non-faces regions. The experimental results on
different types of group color images show that this approach
improves the detection speed and minimizes the false
detection rate in less time and detects faces in different color
images.
@article{rituverma2010robust,
abstract = {Human face detection is a significant problem of
image processing and is usually a first step for face
recognition and visual surveillance. This paper presents the
details of face detection approach that is implemented to
achieve accurate face detection in group color images which
are based on facial feature and Support Vector Machine. In
the first step, the proposed approach quickly separates skin
color regions from the background and from non-skin color
regions using YCbCr color space transformation. After the
detection of skin regions, the images are processed with,
wavelet transforms (WT) and discrete cosine transforms
(DCT) as a result of which the 30×30 pixel sub images are
found. These sub images are then assigned to SVM classifier
as an input. The SVM is used to classify non-face regions from
the remaining regions more accurately, that are obtained
from previous steps and having big difference between faces
regions and non-faces regions. The experimental results on
different types of group color images show that this approach
improves the detection speed and minimizes the false
detection rate in less time and detects faces in different color
images.},
added-at = {2012-10-03T11:09:45.000+0200},
author = {Ritu Verma, Anupam Agrawal and Sharma, Shanu},
biburl = {https://www.bibsonomy.org/bibtex/2053fbe791a80a15d36414e116e70ba74/ideseditor},
editor = {Das, Dr. Vinu V},
interhash = {e62a5e8634b9dbd83dd279360848ea9a},
intrahash = {053fbe791a80a15d36414e116e70ba74},
journal = {ACEEE International Journal on Signal & Image Processing },
keywords = {Face_Detection Wavelet_Transform},
month = {December},
number = 3,
pages = 6,
timestamp = {2013-01-09T06:32:45.000+0100},
title = {A Robust & Fast Face Detection System},
url = {http://doi.searchdl.org/01.IJSIP.1.3.135},
volume = 1,
year = 2010
}