Transcript NSTX-OSFinalPresentation.ppt

Distributed Framework for
Automatic Facial Mark Detection
Graduate Operating Systems-CSE60641
Nisha Srinivas and Tao Xu
Department of Computer Science and Engineering
nsriniva, [email protected]
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Introduction
• What is Biometrics?
– Face, iris, fingerprint etc.
– Face is a popular biometric
• Non-invasive
Different type of Biometric.
– Identical twins have a high degree of facial
similarity.
• Fine details on the face like facial marks are used to
distinguish between identical twins.
– Automatic facial mark detector: detects facial
marks and extracts facial mark features.
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Automatic Facial Mark Detector
Independent of results from other images
Convert Images
Face Contour
Points
Crop face Images
Detect facial marks
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Objective
• Drawbacks of the Automatic Facial Mark Detector
– Slow
• Size of the dataset
• Size of each image in the dataset
• Run time of algorithms is long
• Executing it sequentially
• Objective:
– To design a distributed framework for the
automatic facial mark detector.
• To improve computation time
• To obtain scalability
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Sequential Execution
Input Image
Conversion
Contour Points
Cropping
Execution Time:
Te=Ntp
tp= time to execute
facial mark detector for
a single image
N= Number of Images
FM Detections
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Proposed Approach : Distributed Framework
Machine 2
Conversion
Contour Points
Cropping
Execution Time:
Te= tp
tp= time to
execute facial
mark detector for
a single image
FM Detections
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• Implementation
– Combination of Makeflow, Worker Queue , Condor
• Condor is a distributed environment which makes
use of idle resources on remote computers.
• Work Queue is a fault tolerant framework.
– Master/Worker framework.
– Manages Condor
• Makeflow
– Distributed computing abstraction
– Runs computations on WQ
– The computations have dependencies that are
represented by directed acyclic graph (DAG).
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Flow Diagram
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Performance Metrics
• We evaluate the performance of the distributed
framework by computing the following
metrics
– Total execution time
– Node Efficiency
– Scalability
• Weak scaling: Number of jobs proportional to number
of images in dataset.
• Strong scaling: Number of jobs is varied by keeping the
number of images in the dataset a constant.
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Dataset and System Specifications
• Twin face images were collected at the Twins Days
Festival in Twinsburg, Ohio in August 2009.
• High Resolution Images: 4310 rows x 2868 columns
• Total Number of Images: 800
– Dataset size based on attributes: [206 200 250 144]
• Notre Dame Condor Pool: ~(700 cores)
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Notre Dame Condor Pool
•
•
•
Machine Arch OpSys
ccl00.cse.nd.edu INTEL
ccl01.cse.nd.edu INTEL
MachineOwnerMachineGroup StateLoadAvg Memory
LINUX dthain
ccl Unclaimed 0.190
1518
LINUX dthain
ccl Unclaimed 0.150
1518
Makeflow was executed on cvrl.cse.nd.edu
Intel(R) Xeon(R) CPU
X7460 @ 2.66GHz
CSE
170
compbio
1x8
ccl 8x1
Fitzpatrick
130
Timeshared
Collaboration
CHEG
25
Nieu
20
EE
10
DeBart
10
Personal
Workstations
Storage
Research
netscale
16x2
Network
Research
cclsun
16x2
Storage
Research
netscale
1x32
loco
32x2
cvrl
32x2
sc0
32x2
Network
Research
Batch
Capacity
Biometrics
Hadoop
green
house
iss
44x2
MPI
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Experiments
• Experiment 1
– Comparison of total execution time between the
distributed framework and sequential framework.
– Submit N jobs to Condor by keeping the dataset
constant.
– Number of jobs workers for distributed
framework= {10,50, 100, 150, 200}
– Dataset Size= 206
– Executed on the Notre Dame Condor Pool.
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• Experiment 2
– To evaluate node efficiency
– Analyze the time taken for a single job to complete
on a machine in the Notre Dame Condor Pool.
• Experiment 3
– To evaluate scalability of the AFMD
• Weak scaling: Number of jobs proportional to number
of images in dataset.
• Strong scaling: Number of jobs is varied by keeping the
number of images in the dataset a constant.
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Time (secs)
Experiment 1: Results
Number of Workers
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Time (secs)
Number of jobs executed per
machine
Experiment 2: Results
Number of Workers
Machine Names
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Time (secs)
Experiment 3:Weak Scaling
Number of Workers
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Conclusion
• Designed and implemented a distributed
framework for a Automatic facial mark
detector.
• It was implemented using Makeflow, Work
Queue and Condor.
• Performance of the distributed framework is
significantly better.
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