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PRELIMINARY DESIGN REVIEW HiMax: Facial Biometrics With a CogniMem Device (Neural Processor)
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Presentation Overview: Team and Member Introduction Brief Project Overview Approaches – Phase 1 – Phase 2 – Phase 3 Possible Problems (guarantee there will be some!) End of Semester Project Goals Project Timeline Presentation Summary References Q&A session
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Advisor: Dr. Tep Dobry Sub Advisor: Dr. Neil Scott Dr. Winyu Chinthammit Members: Raymundo Flores James Cuaresma PRELIMINARY DESIGN REVIEW HiMax: Biometrics With a CogniMem Device (Neural Processor)
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Project Overview: This technology is fairly new, so we propose: 1. Research methodology for an “efficient training” of the CogniMem Neural processor. 2. Concurrently, develop “reproducible procedures” for a "high-level confidence" for “Facial Biometric recognition” application in a static physical environment. 3. As needed, create hardware interface with the device; develop software for a particular application.
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Block Diagram: Controlled Environment Diagram Side View 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
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Block Diagram: Controlled Environment – Scripted Matrix Call-out TEST 1: Lighting Variation Effect
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Block Diagram: Controlled Environment – Scripted Matrix Call-out TEST 1: Lighting Variation Effect (Continuation)
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Block Diagram: Controlled Environment – Scripted Matrix Call-out TEST 2: External Facial Accessories Variation Effect
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Block Diagram: Controlled Environment – Scripted Matrix Call-out TEST 3: Facial Change and Facial Emotions Variation Effect
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Block Diagram: CogniMem Device and Applications
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Block Diagram: CogniMem Device (Details)
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Block Diagram: Laptop Temporary Setup & Training (CogniSight Control Panel)
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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 (in-progress). Phase 2 With lesson learned in Phase 1: Will implement a controlled-group “open environment” characterization/testing of the device (more elaboration is just a sec). Phase 3 Verification and “variable-environment” testing of the controlled-group (8 individuals) to the general population False-Positive Control group (thru external facial manipulation) to deceive & create “Positive Unknown” identification
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Phase 1: Results Result Discussion: o Lighting Variation Effects o External Facial Accessories Variations Effect o Facial Changes Effects o Facial Expression Effects
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Phase 1: Lesson Learned o Perform training with “sufficient lighting” o “Zoom-in” ROI size to facial contour. o If possible, set camera configurations to “manual mode” (i.e. shutter speed, gain, etc.) o First neuron committed should be on the background environment. o CogniMem device is somewhat unstable. - Froze 3X (after 10, 2 & 40 minutes of operation). - Uncontrolled neuron assignment.
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What’s next? Phase 2 & Phase 3 Phase 2 With lesson learned in Phase 1: Will implement a controlled-group “open environment” characterization/testing of the device (more elaboration is just a sec). Concurrently, research cause for the o CogniMem to “freeze” o Uncontrolled neuron assignment o USB port assignment erratic
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What’s next? Phase 2 & Phase 3 Phase 3 Verification and “variable-environment” testing of the controlled-group (8 individuals) to the campus general population False-Positive Control group (thru external & facial manipulation) to deceive & create “Positive Unknown” identification
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Problems (Yes, we have them): Hardware & Software o CogniMem to become unresponsive o Uncontrolled neuron assignments (without any keyboard or mouse activity); normally happens right after the CogniMem device becomes unresponsive. o USB port assignment erratic
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End of Semester Project Goals: Our projected goals are to: 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.
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Project Timeline 9/15-9/269/27-10/1010/11-10/2410/25-11/711/8-11/2111/22-12/512/6-12/12 Presentations Proposal Complete PDR Complete CDR Final Tasks Obtain Lab Complete Set up Lab Complete Familiarize with Complete Cognimem (James) Complete Paliminary testing Complete Training on volunteers Started Known to unknown ratio testing
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?Any Questions?
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