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FINAL PRESENTATION HiMax: Facial Biometrics With a CogniMem Device (Neural Processor)

Team introduces research methodology for efficient training of CogniMem Neural Processor, aiming to develop reproducible procedures for facial biometric recognition in controlled environments. Following phases, problems, and goals discussed.

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FINAL PRESENTATION HiMax: Facial Biometrics With a CogniMem Device (Neural Processor)

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  1. FINAL PRESENTATIONHiMax: Facial BiometricsWith a CogniMem Device(Neural Processor)

  2. Presentation Overview: • Team and Member Introduction • Brief Project Overview • Block Diagrams - Controlled Environment (Standard and Variable Settings) - CogniMem Device and Software Development Kits - CogniMem Device (Details) - Laptop Temporary Setup & Training (CogniSight Control Panel) Approaches • Phase 1 (Completed); Discussion of results and “lesson-learned” • Phase 2 (ON-HOLD); Controlled Group Characterization • Phase 3 (ON-HOLD): Un-controlled Group (Noise) Introduction • Phase 4 (Maybe): It depends on Phase 2 & 3 results. • Got Problems!? (Yes, we hadsome!) • End of Semester Project Goals • Project Timeline • Presentation Summary • Q&A session

  3. FINAL PRESENTATIONHiMax: BiometricsWith a CogniMem Device(Neural Processor) Members: Raymundo Flores James Cuaresma EE Advisor: Dr. Tep Dobry ICS-Sub Advisors: Dr. Neil Scott Dr. Winyu Chinthammit

  4. Project Overview: This technology is fairly new, so we propose: • Research methodology for an “efficient training” of the CogniMem Neural processor. • Concurrently, develop “reproducible procedures” for a "high-level confidence" for “Facial Biometric recognition” application in a static physical environment. • As needed, create hardware interface with the device; develop software for a particular application.

  5. Block Diagram:Controlled Environment Diagram Top View Standard Settings: - Method of training: Easy-Video Recognition - Image Recognition Type: Moderate - Region Of Interest (ROI): (244,126)-(208,195) - Light elevations: 65.5”Variable Settings - Refer to Scripted Matrix Call-out Side View

  6. Block Diagram:CogniMem Device and Applications

  7. Block Diagram:CogniMem Device (Details)

  8. Block Diagram:Laptop Temporary Setup & Training (CogniSight Control Panel)

  9. Approaches: • Phase 1 (Completed) • Create a “controlled environment” to characterize the Biometric limits of the CogniMem device with respect to known environmental changes (more elaboration is just a sec). • “Lesson learned “ comprehensive review of result (completed). • Phase 2 • With lesson learned in Phase 1 and suggestions from advisors, will implement a controlled-group “open environment” characterization/testing of the device (more elaboration is just a sec). • Phase 3 • Introduce testing of the uncontrolled-group (False-Positives) • Phase 4 • Control group & Uncontrolled Group unscripted open-environment testing.

  10. Phase 1: Results • Result Discussion: • Lighting Variation Effects • External Facial Accessories Variation Effect • Facial Change Variation Effects • Facial Expression Variation Effects

  11. Phase 1: Lesson Learned • Perform training in “general office lighting environment” • “Zoom-in” ROI size to facial contour. • If possible, set camera configurations to “manual mode” (i.e. shutter speed, gain, etc.) • First neuron committed should be on the background environment. • CogniMem device is somewhat unstable. - Froze 3X (after 10, 2 & 40 minutes of operation). - Uncontrolled neuron assignment.

  12. Phase 1:Region Of Interest Phase 2:Region Of Interest

  13. What’s next?Phase 2 (On-Hold) • Phase 2 • With lesson learned in Phase 1: • Will implement a controlled-group “open environment” characterization/testing/modified deception testing of the device. • Concurrently, research causes for the • CogniMem to “freeze” • Uncontrolled neuron assignment • USB port assignment erratic

  14. What’s next?Phase 3 • Phase 3 • Introduction of uncontrolled group (noise) to characterize “False-Positive” recognition. • Control group (thru external & facial manipulation) to deceive & create “Positive  Unknown” identification

  15. What’s next?Phase 4 • Phase 4 • Control group (with external & facial deception) and uncontrolled group unscripted open environment testing. • Real life simulation.

  16. Problems (Yes, we have them): • Hardware & Software • CogniMem becomes unresponsive • Uncontrolled neuron assignments (without any keyboard or mouse activity); normally happens right after the CogniMem device becomes unresponsive. • USB port assignment erratic • Legal (Privacy) Issues • Privacy Act of 1974 • Letters and Forms • Consent Letter • Protection, handling, storage, and use of collected ‘digital facial biometric information’. • Inter-Disciplinary Dynamics (EE-ICS) • Professional conflict goal resolution.

  17. End of Semester Project Goals: Our projected goals are: • Develop efficient training methodology of the CogniMem device. • Develop “reproducible procedures” for a "high-level confidence" Biometric recognition training. • Have a working model for a static Facial Biometric identification that is reliable. • Hopefully, find solutions to the hardware and software problems we are experiencing to have a more stable system. • EE496: Lay the ground-work for a “dynamic” Facial/Body Biometric follow-on project.

  18. ?Any Questions?

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