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Distributed Framework for Automatic Facial Mark Detection

Distributed Framework for Automatic Facial Mark Detection. Graduate Operating Systems-CSE60641 Nisha Srinivas and Tao Xu Department of Computer Science and Engineering n sriniva , txu1@nd.edu. Introduction. What is Biometrics? Face, iris, fingerprint etc. Face is a popular biometric

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Distributed Framework for Automatic Facial Mark Detection

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  1. Distributed Framework for Automatic Facial Mark Detection Graduate Operating Systems-CSE60641 Nisha Srinivas and Tao Xu Department of Computer Science and Engineering nsriniva, txu1@nd.edu

  2. 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.

  3. Automatic Facial Mark Detector Independent of results from other images Face Contour Points Convert Images Crop face Images Detect facial marks

  4. 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

  5. Sequential Execution Conversion Input Image Contour Points Cropping Execution Time: Te=Ntp tp= time to execute facial mark detector for a single image N= Number of Images FM Detections

  6. Proposed Approach : Distributed Framework Machine n Machine 1 Machine 2 Conversion Execution Time: Te= tp tp= time to execute facial mark detector for a single image Contour Points Cropping FM Detections

  7. 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).

  8. Flow Diagram

  9. 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.

  10. 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)

  11. Notre Dame Condor Pool • MachineArchOpSysMachineOwnerMachineGroupStateLoadAvgMemory • ccl00.cse.nd.edu INTEL LINUX dthain ccl Unclaimed 0.190 1518 • ccl01.cse.nd.edu INTEL 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 green house netscale 16x2 cclsun 16x2 compbio 1x8 ccl 8x1 Storage Research Network Research Storage Research Timeshared Collaboration Fitzpatrick 130 iss 44x2 loco 32x2 cvrl 32x2 sc0 32x2 CHEG 25 EE 10 netscale 1x32 Nieu 20 DeBart 10 Network Research Batch Capacity Biometrics Hadoop Personal Workstations MPI

  12. 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.

  13. 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.

  14. Experiment 1: Results Time (secs) Number of Workers

  15. Experiment 2: Results Number of jobs executed per machine Time (secs) Number of Workers Machine Names

  16. Experiment 3:Weak Scaling Time (secs) Number of Workers

  17. 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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