20-03-2014, 12:05 PM
Face Detection Project Report
[attachment=60300]
Abstract
Human face detection by computer systems has become a major field of interest. Face
detection algorithms are used in a wide range of applications, such as security control, video
retrieving, biometric signal processing, human computer interface, face recognitions and
image database management. However, it is difficult to develop a complete robust face
detector due to various light conditions, face sizes, face orientations, background and skin
colors. In this report, we propose a face detection method for color images. Our method
detects skin regions over the entire image, and then generates face candidates based
on a connected component analysis. Finally, the face candidates are divided into human face
and non-face images by an enhanced version of the template-matching method.
Experimental results demonstrate successful face detection over the EE368 training images.
Introduction
There have been many attempts to solve human face detection problem. The early
approaches are aimed for gray level images only, and image pyramid schemes are necessary to
scale with unknown face sizes. View-based detectors are popular in this category, including
Rowley’s neural networks classifier [1], Sung and Poggio’s correlation templates matching
scheme based on image invariants [2] and Eigen-face decomposition [3]. Model based
detection is another category of face detectors [4].
For color images, various literatures have shown that is possible to separate human skin
regions from complex background based on either YCbCr or HSV color space [5, 6, 7, 8]. The
face candidates can be generated from the identified skin regions. Numerous approaches can
be applied to classify face and non-face from the face candidates, such as wavelet packet
analysis [6], template matching for faces, eyes and mouths [8, 9, 10], feature extraction using
watersheds and projections [5].
Color Segmentation
The first step is color segmentation of the image. Several color spaces are available but
Hue-Saturation-Value (HSV) color map is the most adequate for differentiating the skin
regions from the rest of the photo contents. A set of equations that maximize the amount of
skin pixels while minimizing the number of background pixels can be found using the plot of
skin regions vs. non-skin regions in H vs. S, S vs. V and H vs. V. These bounding equations
are used to generate the first binary image. However, some face candidate boxes contained
two people because their black hairs were connected and included as skin region. In order to
balance taking out the hair vs. loosing some of the face skin pixels, the luminance and
chrominance (YCbCr) color space is also used to differentiate the black hair pixels from the
skin pixels in case of high luminance images.
Template Matching
The template-matching compares the face candidate image with the face template,
measures the level of similarity and concludes whether it is human face or a non-face. Several
enhancements have been made to optimize the template-matching algorithm for the training
images given by the EE368 instructors. A multi-layer classification scheme has been
implemented to avoid missing faces or having non-faces. The color space chosen for the
template matching is gray because the best results have been experimentally obtained. The
template matching algorithm loads the face and non-face template images, it computes the 2
dimensional (2-D) cross-correlation or the 2-D convolution.
The face template is an image made by averaging all faces on the training images.
Figure 4-1 shows the used Face Template image.
Conclusion
We have presented a face detection algorithm for color images that uses color
segmentation, connected component analysis and multi-layer template-matching. Our method
uses the color information in HSV space, compensates for the luminance condition of the
image, and overcomes the difficulty of separating faces that are connected together using
image morphology processing. Finally, an enhanced version of the template-matching
algorithm is used to detect all human faces and reject the non-faces such as hands and clothes.
Experimental results have shown that our approach detected 164 out of 165 faces present
in the seven project training images (half faces are classified as non-faces). The only one
missing face is due to very dark glasses. No false alarms are raised in any of the seven images.
The average run time on ISE lab workstation is ~12 seconds.
Future work will be focused on verifying the algorithm performance against general
images and studying the required modifications to make the algorithm robust with any image.
[attachment=60300]
Abstract
Human face detection by computer systems has become a major field of interest. Face
detection algorithms are used in a wide range of applications, such as security control, video
retrieving, biometric signal processing, human computer interface, face recognitions and
image database management. However, it is difficult to develop a complete robust face
detector due to various light conditions, face sizes, face orientations, background and skin
colors. In this report, we propose a face detection method for color images. Our method
detects skin regions over the entire image, and then generates face candidates based
on a connected component analysis. Finally, the face candidates are divided into human face
and non-face images by an enhanced version of the template-matching method.
Experimental results demonstrate successful face detection over the EE368 training images.
Introduction
There have been many attempts to solve human face detection problem. The early
approaches are aimed for gray level images only, and image pyramid schemes are necessary to
scale with unknown face sizes. View-based detectors are popular in this category, including
Rowley’s neural networks classifier [1], Sung and Poggio’s correlation templates matching
scheme based on image invariants [2] and Eigen-face decomposition [3]. Model based
detection is another category of face detectors [4].
For color images, various literatures have shown that is possible to separate human skin
regions from complex background based on either YCbCr or HSV color space [5, 6, 7, 8]. The
face candidates can be generated from the identified skin regions. Numerous approaches can
be applied to classify face and non-face from the face candidates, such as wavelet packet
analysis [6], template matching for faces, eyes and mouths [8, 9, 10], feature extraction using
watersheds and projections [5].
Color Segmentation
The first step is color segmentation of the image. Several color spaces are available but
Hue-Saturation-Value (HSV) color map is the most adequate for differentiating the skin
regions from the rest of the photo contents. A set of equations that maximize the amount of
skin pixels while minimizing the number of background pixels can be found using the plot of
skin regions vs. non-skin regions in H vs. S, S vs. V and H vs. V. These bounding equations
are used to generate the first binary image. However, some face candidate boxes contained
two people because their black hairs were connected and included as skin region. In order to
balance taking out the hair vs. loosing some of the face skin pixels, the luminance and
chrominance (YCbCr) color space is also used to differentiate the black hair pixels from the
skin pixels in case of high luminance images.
Template Matching
The template-matching compares the face candidate image with the face template,
measures the level of similarity and concludes whether it is human face or a non-face. Several
enhancements have been made to optimize the template-matching algorithm for the training
images given by the EE368 instructors. A multi-layer classification scheme has been
implemented to avoid missing faces or having non-faces. The color space chosen for the
template matching is gray because the best results have been experimentally obtained. The
template matching algorithm loads the face and non-face template images, it computes the 2
dimensional (2-D) cross-correlation or the 2-D convolution.
The face template is an image made by averaging all faces on the training images.
Figure 4-1 shows the used Face Template image.
Conclusion
We have presented a face detection algorithm for color images that uses color
segmentation, connected component analysis and multi-layer template-matching. Our method
uses the color information in HSV space, compensates for the luminance condition of the
image, and overcomes the difficulty of separating faces that are connected together using
image morphology processing. Finally, an enhanced version of the template-matching
algorithm is used to detect all human faces and reject the non-faces such as hands and clothes.
Experimental results have shown that our approach detected 164 out of 165 faces present
in the seven project training images (half faces are classified as non-faces). The only one
missing face is due to very dark glasses. No false alarms are raised in any of the seven images.
The average run time on ISE lab workstation is ~12 seconds.
Future work will be focused on verifying the algorithm performance against general
images and studying the required modifications to make the algorithm robust with any image.