distributed framework for automatic facial mark detection
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Distributed Framework for Automatic Facial Mark Detection
Graduate Operating Systems-CSE60641Nisha Srinivas and Tao Xu
Department of Computer Science and Engineeringnsriniva, txu1@nd.edu
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Introduction
• What is Biometrics?– Face, iris, fingerprint etc.– Face is a popular biometric• Non-invasive
– 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.
Different type of Biometric.
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Automatic Facial Mark Detector
Convert Images Face Contour Points
Crop face Images Detect facial marks
Independent of results from other images
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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
4
Sequential Execution
Execution Time:Te=Ntp
tp= time to execute facial mark detector for a single imageN= Number of Images
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Conversion
Contour Points
Cropping
FM Detections
Input Image
Proposed Approach : Distributed Framework
Conversion
Contour Points
Cropping
FM Detections
Machine 1 Machine n
Machine 2
Execution Time:Te= tp
tp= time to execute facial mark detector for a single image
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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
8
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 ArchOpSys MachineOwnerMachineGroup StateLoadAvg Memory
• ccl00.cse.nd.eduINTELLINUXdthaincclUnclaimed0.1901518
• ccl01.cse.nd.eduINTELLINUXdthaincclUnclaimed0.1501518
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ccl 8x1cclsun16x2
loco32x2
sc032x2
netscale16x2
cvrl32x2
iss44x2
compbio1x8
netscale1x32
Fitzpatrick130
CSE170
CHEG25
EE10
Nieu20
DeBart10
MPIHadoopBiometrics
StorageResearch
NetworkResearch
NetworkResearch
TimesharedCollaboration
PersonalWorkstations
StorageResearch
BatchCapacity
greenhouse
Makeflow was executed on cvrl.cse.nd.eduIntel(R) Xeon(R) CPU X7460 @ 2.66GHz
Experiments
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• 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.
• 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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Experiment 1: Results
14Number of Workers
Tim
e (s
ecs)
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Experiment 2: Results
Machine NamesNumber of Workers
Tim
e (s
ecs)
Num
ber
of jo
bs e
xecu
ted
per
mac
hine
Experiment 3:Weak Scaling
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Tim
e (s
ecs)
Number of Workers
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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