International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
A Performance Study on Face Recognition Mechanism
using Visible and Thermal Infrared Imagery
* K.Purushotham
Mosire Mahipal 2
Professor, Dept. of ECE, Jigjiga University, Ethiopia, Africa
2Dept. of ECE, Kakatiya Institute of Technology & Science for Women, Nizamabad, AP, India
1
1Assoc
Abstract: Facial skin temperature is closely related to the underlying blood vessels; thus by obtaining a thermal map
of the human face we can also extract the pattern of the blood vessels just below the skin. Thermal Infrared Imaging
has many applications in different scientific, engineering, research, and medical areas. This paper presents
preliminary findings using thermal infrared imaging for the detection of the human face vasculature network at the
skin surface and the generation of thermal facial signatures. The thermal infrared images were then analyzed using
digital image processing techniques to enhance and detect the facial vasculature network of the volunteers and
generate a thermal facial signature for each volunteer. We further present a comprehensive performance analysis of
multiple appearance-based face recognition methodologies, on visible and thermal infrared imagery. The effect of
illumination conditions on recognition performance is emphasized, as it underlines the relative advantage of radio
metrically calibrated thermal imagery for face recognition.
Keywords: Face Recognition, Biometrics, Thermal facial signatures, Infrared Imagery, Eigenfaces.
visible video equipment, lower image resolution,
1. Introduction
A thermal infrared camera with reasonable
higher image noise, and lack of widely available
sensitivity provides the ability to image superficial
data sets. These historical objections are becoming
blood vessels on the human skin. The experiment
less relevant as infrared imaging technology
presented here consists of the image processing
advances, making it attractive to consider thermal
techniques used in thermal infrared images
sensors in the context of face recognition.
captured using a mid wave infrared camera from
2. Proposed Thermal Face Recognition
FLIR systems. For the purpose of this experiment
The temperature variation across the face can be
thermal images were obtained from 10 volunteers,
easily visualized as the different colour regions in the
they were asked to sit straight in front of the
thermogram. In Fig. 2 yellow colour represents average
thermal infrared camera and a snapshot was taken
temperature of 97.30F, bright green colour represents
of their frontal view. Face recognition in the
average temperature 95.10F, brown colour represents
thermal infrared domain has received relatively
average temperature of 93.70F, blue colour represents
little attention in the literature in comparison with
average temperature of 89.60F and pink colour
recognition in visible-spectrum imagery. Original
represents
tentative
mostly on
Physiological features of a thermal face image have
validating thermal imagery of faces as a valid
already been discussed in details in the introduction
biometric. The lower interest level in infrared
section. In this work, these characteristics have been
imagery has been based in part on the following
used to build a unique thermal face print of a person
analyses
have
focused
average
temperature
of
91.40F.
factors: much higher cost of thermal sensors versus
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
extract lines similar to isothermal lines in weather maps
linking all points of equal or constant temperature, in
order to get blood perfusion data. Fig. 5 shows blood
perfusion image of a thermal face image. The extracted
blood perfusion image is nothing but border area of two
different regions.
Figure 1 Thermal Face Image Example
In this method we extract the face skin region from
grayscale thermal (infrared) image. Initially, each of the
captured 24-bits colour images have been converted
into its 8-bit grayscale image counterpart. Then convert
binary image from converted grayscale image. The
resultant image replaces all pixels in the grayscale
image with luminance greater than mean intensity with
Figure 2 Proposed Method
the value 1 (white) and replaces all other pixels with the
value 0 (black). Different 8-connected objects present
in 2-D binary image have been extracted
and the
largest component among them has been identified as
face skin region and other small components, which are
other parts of the image, have been rejected. Fig. 3,
depicted the largest component as a face skin region.
There after crop the image is cropped and unwanted
part of the image i.e. the background is eliminated. In
Figure 3 The largest component as face skin region
with black back ground
binary image, black pixels mean background and white
pixels mean the face region. Cropping process starts
from top left corner and top right corner of the binary
image along the lines and traverses parallel to vertical
axis. This process stops when it encounters a white
pixel first and then draw a vertical line from two points
(one is left side of the face and another is right side of
Figure 4 Cropped face image
the face), eliminates the left part and right part (i.e.
black pixel) of the lines respectively. In the same way it
eliminates the upper and lower side of the face region.
Fig. 4 shows the cropped image. Morphological erosion
is used to extract the thermal physiological face features
and construct the region having constant or equal
temperatures. Medial axis transform [9] is applied to
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crucial for accurate face recognition. The more spurious
minutiae are eliminated; the better will be the
classification performance. In addition, classification
time will be significantly reduced because reduction of
feature points. Thus the whole image is divided into
number of fixed sized blocks. The size of the each
block may be 8×8, 16×16, and 32×32. The number of
Figure 5 Blood perfusion image
minutiae points in each block have been counted and
The concept of minutiae extraction from finger
stored in a vector. Fig. 8 illustrates minutiae points,
print recognition have been taken and applied to our
which are basically the bifurcation and termination
work. Here fingerprint‘s ridges are like blood perfusion
points, using green and red colors respectively. For
data of a face. The uniqueness of a face‘s blood
each face image, one corresponding vector has been
perfusion data can be determined by the pattern of
found i.e. total number of such vectors is equal to total
ridges as well as the minutiae points. Minutiae points
number of faces. Then divide these vectors into two sets
are local ridge characteristics that occur either at a ridge
one for training purpose and another for testing
bifurcation or at a ridge termination.
purpose. ANN classifier has been used to classify each
of these vectors [2],[3]. Here a five layer feed-forward
back propagation neural network has been used for this
purpose. First hidden layer contain 100 neurons, second
hidden layer contain 50 neurons, and third hidden layer
Figure 6 Binary number indicating the minutiae
point
The number of ‗1‘‘s within each 3x3 window on
the blood perfusion image where minutiae points are
essentially the terminations and bifurcations of the
ridge lines that constitutes a faceprint is computed. This
is the vital part of the minutiae extraction of the
holds 10 neurons and the last layer contain 6 neurons
because 6 different persons are there in our experiment.
Tan-sigmoid transfer functions is used to calculate a
layer‘s output from its net the first input and the next
three hidden layers and the outer most layer gradient
descent with momentum training function is used to
updates weight and bias values.
3. Results & Conclusions
faceprint image where the termination point and
bifurcation point will be determined. If the central cell
has a ‗1‘ and has another ‗1‘ as it‘s only neighbours,
then it is a termination point like in Fig. 7a. If the
central cell contains a ‗1‘ and has three ‗1‘‘s as
neighbours, then it is an bifurcation as shown in Fig. 7b
and if the central cell is ‗1‘ and has two ‗1‘‘s as
neighbours, then it is a normal point like in Fig. 7c. Due
to various noises in the face print image, the extraction
algorithm produces a large number of spurious minutiae
points. Therefore, differentiating spurious minutiae
We have performed Experiments on our own
thermal face images captured using a FLIR 7 camera.
Some preprocessing has been done for the image
database used in this paper. A typical thermal infrared
face image is represented by different colour regions
with different temperatures which, has been already
discussed in details in the section II. All the training
and testing images are grayscale images of size
416×544. Face images of 6 persons (each having 34
different images) were captured in normal temperature
from real minutiae in the post-processing stage is
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
conditions till today. The obtained results are shown in
Table 1 for different block sizes.
In thermal face recognition proposed here, Entire
face image is divided into equal number of blocks and
the total number of minutiae points from each block is
considered as one feature. Features from all the blocks
are combined to create the final feature vector.
Classification of these feature vectors has been done
using a multilayer perception. Final recognition rate has
been enhanced by varying size of the blocks. One of the
major advantages of this approach is the ease of
implementation.
Furthermore,
no
knowledge
of
geometry or specific feature of the face is required.
However, this system is applicable to front views and
constant background only. It may fail in unconstraint
References:
[1]
B.Toth,
―Biometric
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Information Security Bulletin, October 2005.
[2]
S. De Geef, P. Claes, D. Vandermeulen, W.
Mollemans, and P.G. Willems, ―Large-Scale InVivo Caucasian Facial Soft Tissue Thickness
Database for Craniofacial Reconstruction,‖ Forensic
Science, vol. 159, no. 1, pp. S126-S146, May 2006.
[3]
S.G. Kong, J. Heo, B.R. Abidi, J. Paik, M.A.
Abidi, Recent advances in visual and infrared face
recognition—a review, Comput. Vision Image
Understanding 97 (2005) 103–135.
[4]
R.C. Gonzalez and R.E. Woods, Digital Image
Processing, 3rd Edition, Prentice Hall, 2002
[5]
M. Turk and A. Pentland, ―Face recognition
using eigenfaces‖, Proc. IEEE Conf. on Computer
environments like natural scenes.
Table 1 PERFORMANCE RATE FOR
DIFFERENT BLOCK SIZE.
Vision and Pattern Recognition, pp. 586-591, 1991.
Authors Profile:
Next, our goal is to improve the filtering process
for
the
wavelet
coefficients and recombination
K.Purushotham is Presently
working in Jigjiga University,
Ethiopia, Africa as an Associate
Professor. He got his Masters
degree, M.Tech in SSP from
JNTUH, Hyderabad. He has an
experience of over 8 years in
teaching research and industry.
technique so we can eventually reconstruct the entire
pulse signal in the time domain using data from all
measurement sites. Higher accuracy similar to the ECG
specifications is also desirable. Finally, a new method
to determine accuracy is needed to account for the heart
rate variability heart rate over time, which will
preferably work directly by using the wavelet
coefficients.
Acknowledgements
The
authors
would
like
Mosire Mahipal, working as
Assistant professor in Presently
working in Kakatiya Institute of
Technology & Science for
Women, Nizamabad, A.P. He has
over seven years of experience in
industry, research and teaching.
to thank the
anonymous reviewers for their comments which were
very helpful in improving the quality and presentation
of this paper.
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