![](bg2.png)
matching by using the cross correlation between the
histogram of the input face and the histograms of the
faces in the training set. The results obtained from
the proposed histogram based system provide a
recognition rate as high as 99.60% for the ORL face
database when 5 poses of each subject out of 40
persons are used to train and remaining 5 poses are
used for performance testing. The proposed method
clearly outperforms the classical face recognition
systems such as PCA based eigenfaces and LDA
based fisherfaces methods, where this rate is down
to 78% and 87% respectively.
Additionally, due to the high correlation between
the histograms of the faces at different scales, the
proposed system is robust for scale changes. The
recognition rate for smaller faces with sizes of only
30% of the images in the training set is 98.22%,
which is within 2% reduction in the recognition rate.
This result is expected, because the information loss
measured by entropy encountered by image
shrinking down to 30% of the original image is in
the range of 2%.
2 HISTOGRAM BASED FACE
RECOGNITION
One of the methods of describing an image in lower
dimension is using histogram. Histogram of an
image can be considered as feature vector
representing of the image. In general, histogram of
an image is a statistical description of the
distribution in terms of occurrence frequencies of
pixel intensities. The size of the image histogram
depends on the number of quantization levels of the
pixel intensities. Typical monochrome image with 8-
bit representation has 256 gray levels. In a
mathematical sense, an image histogram is simply a
mapping
i that counts the number of pixel
intensity levels that fall into various disjoint
intervals, known as bins. The bin size determines the
size of the histogram vector. In this paper the bin
size is assumed to be 256 and the size of the
histogram vector is 256. Histogram of a
monochrome image,
i, meets the following
conditions
∑
=
=
255
0i
i
N
η
(1)
where N is the number of pixels in an image. Then,
histogram feature vector, H, is defined by,
],,,[
25510
"=H
(2)
The similarity between two images can be
measured by using the cross correlation between the
histograms of the respective images. The maximum
correlation coefficient in the correlation vector is
taken as the measure of similarity and used in the
histogram matching process.
If H1,H2,….,HM be a set of raining face images
with different poses and M be the number of image
samples, then a given query face image, the
histogram of the query image Hq can be used to
calculate the correlation between Hq and histograms
of the images in the training samples as follows:
max(),,
iiq
HH i M
χ
==1D",
(3)
Thus, the similarity of the ith images in the
training set and the query face can be reflected
by
i
, the maximum cross correlation coefficient.
The, image with the highest similarity measure, is
declared to be the identified image in the set.
The proposed system using histogram as the face
feature vector and maximum cross correlation
coefficient as the histogram matching measure is
tested on Head Pose face database (Gourier, 2004),
which contains 15 subjects with 10 selected different
poses. The face dataset is divided into training set of
n (n≤5) images per subject and the rest images for
the test set. The images used in the test set are not
included in the training set. The correct recognition
rates in percent are included in Table 1. Each result
is the average of 500 runs, where we have randomly
shuffled the faces in each class. The results of the
proposed system are outstanding, because even a
single image in the training set provides a correct
recognition rate as high as 94.89%. This rate is down
to 68.89% in the PCA based face recognition
systems respectively.
The proposed method shows slight improvement
as the number of training set images is increased. On
the other hand PCA based systems reaches 92%. The
results are very encouraging and the proposed face
recognition system shows a clear superiority over
the conventional face recognition systems.
Table 1: Performance of the proposed histogram based
system compared with PCA based systems.
# of Training
Images
PCA
Proposed Histogram
Matching Method
1 68.89 94.89
2 81.67 94.62
3 89.52 98.14
4 92.22 97.33
5 92.00 98.80
POSE INVARIANT FACE RECOGNITION USING IMAGE HISTOGRAMS
283