Video based Parallel Face recognition using Gabor
filter on homogeneous distributed systems
Usman Ali
Muhammad Ali Jinnah University,
Islamabad Campus, Pakistan
usma giin abot~otmail.com
Abstract - This research aimed at building a fast video, parallel face recognition system based on the well known Gabor
filtering approach. Face recognition is done after face detection in each frame of the video, individually. The master-slave
technique is employed as the parallel computing model. Each
frame is processed by different slave Personal Computers (PC)
attached to the master, which acquire and distribute frames. It
is believed that this approach can be used for practical face
recognition applications with some further optimization.
Index Terms - Gabor Filter, Parallel image processing,
Cluster computing, Video based face recognition.
I. INTRODUCTION
Face
recognition isanimportanttaskofcomputervi.
Face
recognition
iS an important task of computer vision.
In order to improve the recognition performance, enriched
information to represent each person must be extracted.
One promising direction is to use image sequences of faces
resulting from head, lips, eye etc movements. Recent psychophysical results show that humans make use of face
movement information for recognition.
A. Related work in Image-based Face Recognition:
Face recognition using visual images is gaining acceptance as a superior biometric [1]. Images taken from visual
band are formed due to reflectance. Therefore they depend
on the external light source, which sometimes might be
absent e.g. night time or when there are heavy clouds. Imageryy is also difficult because it depend on the intensity of
l
.
light and ane comofncid sen
ofacelight
Some of the commonly used face recognition techniques
,ager
Principal Component Analysis (PCA) [2], Independent
PCa)[is ndependent
rincipalComponent Analysis (
sis (LDA) [4], and Elstic [3unch Graph Matching (EBGM)
sis.
G
M hM
are
Muhammad Bilal
COMSATS Institute of Information Technology,
Abbottabad Campus, Pakistan
mbilal19@a Ocm
ognition is to prevent the fraudulent system penetration by
pre-recorded facial images. The great difficulty to forge a
video sequence in front of a live video camera may ensure
the biometric data come from the actual user. Another key
advantage of the video based method is that more informa-
Ion iS available in a video sequence than in a single image.
If the additional information can be properly used, we may
further increase the recognition accuracy.
In [11], a multiple classifiers fusion based video face recognition algorithm is developed. The method preserves all
the spatial temporal information contained in a video sequence. And has it has been reported to achieved recognition rate of 98.6%.
The need to speed up image processing computations
brought parallel processing into the computer vision domain. There are several parallel programming models in
common use: Shared Memory, Threads, Message Passing,
Data Parallel, Task Parallel, and Hybrid.
Besides raw computational needs, other issues must be
addressed to efficiently use parallel processing in solving
vision problems [12], which are: architectures, System integration, programming model and software tools, real time
vision. Two open standards for portable high performance
computing, VSIPL and OpenMP, are utilized for paralled
processing and the performance benefits are demonstrated
in [13]. Speedups achieved were analyzed for different
number of processors.
nube of prcsos
A hybrid and parallel face recognition system based on
artificial neural networks and PCA is presented in [14],
where
a parallel architecture for Artifical neural networks is
(Ld [and Elatei Buchn
[5], Rland
G orkinVideoBased
In [15], computation architecture for an adaptive
Filteringetechniquei[proposed.
B. Related work in Video
Based Face Recognition.
visual information retrieval system is presented. System has
been designed to run as a distributed system using middleOne problem with the image-based method is that it is
ware techniques. It contains methods to dynamically balpossible to use a pre-recorded face photo to pretend as a
ance load among available servers to improve overall syslive subject. The second problem is that the image-based
recognition accuracy is still too low to be used in some
tem performance. In [16] the usefulness of synchronous
practical applications. In order to overcome these problems,
parallel processing for real time image processing is demvideo based face recognition has been proposed recently [7onstrated. A number of basic image processing operators
10]. One of the major advantages of video-based face rectogether with their data parallel implementation are presented. Some of image processing operators explored are:
I$20.OOC2006 IEEE
Region-based Image Operators, Morphologic Operators,
Edge Detection, Point Operators and Local Operators.
A very easy approach of adding parallelism to a sequential image processing library is presented in [17]. The
method parallelizes a framework function of the library
responsible for processing filter operators. Method is validated by testing it with the geometric mean filter. Two new
algorithms have been introduced in [18] to reduce overhead
for data transmission for parallel algorithms requiring
neighbourhood pixels.A parallel face recognition system
recognition system based on the Eigenfaces method is designed in [19] to support a live sequence of input images
from a video camera at approximate rate of 30
frames/second.
In this paper we proposed a parallel video based face recognition on heterogeneous environment. Popular technique
of gabor filter is used for face recognition in each individual
frame of the video. Paper is organized as follow: In section
2 Face skin detection is discussed. Section 3 and 4 discusses Feature point calculation and Feature point selection
respectively. Feature vector generation, Similarity calculation in proposed architecture is presented in subsequent
sections. Finally, the results and conclusions are discussed
II. GABOR FILTERING FOR FACE RECOGNITION
A. Face Skin Detection
Once a face is detected in a video frame, the conventional
image based face recognition technique will be used for
single frame recognition. Skin color based detection [20] is
implemented in the proposed architecture due to its simplicity though it's less efficient.
Skin color range, tested for wide range of skin color, is
( (r>95) & (g>40) & (b>20) &
((max (r,g,b) - min (r,g,b)) > 15) &
(abs(r-g)>1 5) & (r>g) & (r>b))
Where r,g and b are red, green and blue contents of the
pixel respectively.
B. Feature point calculation
Physiological studies found simple cells in human visual
cortex that are selectively tuned to orientation as well as to
special frequency, it was suggested that the response of a
simple cell could be approximated by 2-D Gabor filters
[21]. One of the most successful recognition methods is
based on graph matching of coefficients, which have disadvantages due to there matching complexity.
A filter response at any point can be calculated by convolving the filter kernel with the image at that point. For
point (X, Y), filter response denoted as R is defined as
R1 = x cos(0) + y sin(0)
R2 = -x sin(0) + y cos(0)
N-X-1 M-Y-1
E
E I(X + x, Y + y)f(x, y, 0, A)
X=-x Y=-Y
f (x, y, 0, A, (X, (Y) = e
-O.5(
RI2
OX
+
R22
Y
)
= 0 n,
* k / k 1,2.
Where .X and (TY are the standard deviation of the
Gaussian envelop along the x and y dimensions respectively. ) , 0 and n are the wavelength, orientation and no of
'
orientationsrespectively. I(x,y) denotesNxMimage.
We have chosen four orientations and a constant wavelength because feature points are relatively insensitive to
the Gabor kernel wavelength, while vary significantly
across different orientations. We have constant X = 2*1.414
and TX= (TY= /2.
C. Feature Point selection
We chose the point, in a particular window of size SxT
around which the behavior or response of Gabor filter kernel is maximum, as feature point.
Where S = N
and T = M
Where N = no of columns and M = no of rows and W is
the no of windows.
Feature point located at any point can be evaluated as
Rf(xo, yo) = maX (Rj(x, y))
(x,y)EC
Where Rj is the response of the image to the jth Gabor filter and C is any window.
D. Feature vector generation
Feature vectors are generated at feature points as discussed in previous sections. pth feature vector of ith reference face is defined as:
_
[xp, yp, R, 1(xp, y)
Where j = no of responses. Feature vector contains response with location information.
E. Similarit calculations
Similarity and difference between features of input image
and any image from database is calculated using formulas
provided in [22].
III. VIDEO BASED PARALLEL FACE RECOGNITION:
Acquired face video is first converted in to image
se-
quences. technique
The featurediscussed
points areinthen
calculated
using
filtering
sections
II. For
test Gabor
input
video the person is asked to move his/her face, face lips,
R(X,Y,O,2~~~~~~~~) = ~~eyes, etc. Feature vectors are calculated for each frame (im-
age sequence) of the input video, individually. The parallel
algorithm of this stage can be illustrated in Figure 1.
It is usually desired that the recognition system should be
fast as well as efficient. As, same face recognition steps are
repeated for each image sequence. So it is proposed that
individual face recognition processes for each image sequence should be carried out on different slave computers.
The parallel algorithm is designed based on same master
slave programming model as in [19, 23]. Design of the parallel architecture for video based parallel face recognition is
show in figure 2.
The master processing node acquires the input frames,
distribute the frames to a set of slave nodes, gather feature
vectors from the slaves, determine the closest distance, and
declare the final recognition result. The slave nodes, on the
other hand, compute the feature points. The input frames
are partitioning in the time domain rather than the image
dimension domain. Each slave receives a set of different
input frames (of the same video stream) from the master in
a round robin fashion.
Thus, multiple computers are utilized to speedup the
identification process up to the point, where real-time performance can be achieved.
Feature
Vectors
Database
- - -I-
(A
r-<
:-
- - --L-I- -- -r
r-n
I~~~~~~~~~~~~~~~~~
-
=I 1
r--D-
Face Detection
-
Pre- processing
Feature value
calculation
h i~~~~~Master
-
<
I~
Decisionl
Maker
Feature vector
selection
Slaity
calculation
i|
|
Figure 1: Parallel Video Based Face Recognition Architecture
El
E22
l*,,,,,
21m
Figure 2: Master Slave implementation of video based Face Recognition
A. Distributed implementation protocol
Complete system is consisted of 'n' clients and one
server. All the clients are connected to the server using dif-
ferent ports. Each frame is send to individual 1.2 GHz processor client, which apply Gabor filtering technique on the
image and send similarity calculation of the image, with all
the database images, to the server.
Complete work flow is given below:
m
All the images are ready to send to 'n' clients i-e
the images have been read from 'txt' file format and have
been saved in arrays.
* 'n' number of Clients connect to server.
* Server sends the image file to n number of Clients.
* Server
in
writes
send
times
files
'server_send timeo'.............' server_send_time1,'.
* each client receives each image and write the receive
time in file 'client_recv_time'.
* Each client writes the process start time in file 'cli-
ent_process_start_time'.
* Each client now processes (apply Gabor filter) the image
sent to it.
* After the processing ends each client write the process
end time in file 'client_process_end_time'.
* 'n' number of Clients now send their results to server.
* Server writes the received times in files 'server
_recv_timeo'........ ' server_recv_time11'.
B. Processing Time Calculation
We have 'n' frames images to be send to 'n' clients.
Total time taken to processed 'n' frames=Total Time
Total Time=server send timeo - server_recv_time11
i-e first image send time to client minus last image received
time from Client
Total Time also includes the whole network delays.
Network delay=nd
ndl=server_send_timeo - client_recv_time
nd2=client_send_time -server_recv time0
nd=ndl+nd2
This is the delay that each client cost us, as we are in
distributed environment and it is assumed that same amount
of delay is caused while communication between a clients
and a server.
Time taken by the process at clients=
client_process_end_time - client_process_start_time
IV. LIMITATIONS OF PARALLEL FACE RECOGNITION
A. Complexity:
In general, parallel applications are much more complex
than corresponding serial applications, perhaps an order of
magnitude. The costs of complexity are measured in programmer time in virtually every aspect of the software development cycle: Design, Coding, Debugging, Tuning and
Maintenance. Face recognition result would depend now on
the proper functioning of the all the slaves and master, thus
increasing the complexity.
B. Resource Requirements.
The primary intent of parallel video based face recognition programming iS to decrease execution clock time,
however in order to accomplish this, more CPU time is required. For example, a parallel code that runs in 1 hour on 3
processors actually uses 3 hours of CPU time. Similarly, the
amount of memory required would be greater for parallel
recognition than serial one, due to the need to replicate data.
The overhead costs associated with setting up the parallel
environment, task creation, communications and task termination can comprise a significant portion of the total execution time for short runs.
C. Scalability:
Hardware factors play a significant role in scalability. For
example: Memory-CPU bus bandwidth on an SMP machine, Communications network bandwidth, Amount of
memory available on any given computer. Thus the performance might be reduced; if there is high traffic on network or the systems used as slaves or master have slow
speed.
V. BENEFITS OF PARALLEL FACE RECOGNITION
A. Speedups:
Speedup performance could reach to up to number of sequences used for video based face recognition. If numbers
of frames are 10 then parallel processing through proposed
architecture would take almost 10 times less then the sequential processing time required for processing. Speedup
would also depend on the network traffic, speed of all
slaves and master, memory of all slaves and master, network card speed, network speed etc.
B. Portability:
All of the usual portability issues associated with serial
l fteuulpraiiy susascae ihsra
programs apply to parallel programs. Developed applications for slave and master for face recognition can be run on
any Local Area Network (LAN) and no special arrangements would be required.
VI. CONCLUSIONS
This paper briefly described a parallel design framework
for efficient and real-time video based face recognition system. As efficient video based face recognition system has
high computational cost, and it is even high, if high computational (but efficient) Gabor filtering technique is utilized
for face recognition, the proposed parallel cluster computer
architecture would make the video based face recognition
process both faster and efficient.With our design framework, the real-time performance can be achieved on regular
computers, such as those found in a student cluster.
FUTURE WORK
We are currently working on the implementation
of the proposed design on the four 1.2 GHz processors for
the same database for which non-parallel design has been
demonstrated. Applications for master and slaves are developed in Visual C++. The prototype system would be capa-
ble of reliable recognition, with reduced constraints in regards to the position and orientation of the faces.
ACKNOWLEDGEMENT
We are thankful to Higher Education Commission (HEC)
Pakistan and PTCL Pakistan for facilitating us towards research and providing us travel grant support to attend conferences.
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