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Phase III Gate ReviewBetter Business Butler P19591
Kollin Brakefield, Jed Katz, Marissa McCarthy, Rory McHenry,
Tom Papish, Joel Yuhas
Agenda
● Benchmarking
● Risk Management
● Human Subject Testing
● Test Plans
● Prototyping
○ Prototyping Plan
○ Current Status
○ Demonstration
● Next Steps
Server Infrastructure Design
The server must be able to:
● Store all relevant information
● Compute facial embeddings for detected faces
● Potentially detect faces in larger images
● Route all information to relevant destination
Processor Benchmarking
Edge Link:
Face Recognition BenchmarkingRecognition
SoftwareOnline Price
Open
Source
Real Time
Usage
Age/
GenderEmotions Other
Microsoft API YFree Trial/
PremiumN Y Y Y
Facial Hair,
Glasses
Face Net N Free Y Y N YFacial Hair,
Glasses
Dahua Camera N $1796 N Y Y YGlasses, Mask,Facial
Hair
Amazon AWS YFree Trial/
PremiumN Y Y Y
Sunglasses,
Eyes/Mouth Open
Visage SDK N ~$2800 Y Y Y YEye Status,
Graze Tracking
Human Subjects Testing● Waiting on confirmation of research or project classification - Heather Foti
● Will require individual consent form for each subject (including members
of team and other individuals involved with team) regardless of research
or project status
● If the testing is classified as research it will require review by RIT
Institutional Review Board (IRB)
● The team will still maintain certain IRB requirements including; informed
consent without deception, maintaining privacy of subjects, and
confidentiality of data.
● Use of protected groups will be avoided. Protected groups are children,
pregnant women, and prisoners.
Risk Management
Takeaway: Human Subject Research insights have lead the team to recognize an additional risk
Edge Link:
Test Plans
S1 & S3 Test Plan Test Goal:
● Ensure the time from when a face is detected to when embedding and compression of image is complete is less than 5 seconds
● Test system's ability to ignore noise by testing with “null” subjects with a false-positive rate of 5% at worst
Testing Parameters
● Sample Size = 80 ○ 6 human subjects with 12 runs per subject & 8 null
subjectsLink to edge
Test Goal:
● Test storage capabilities of system and
ability to choose the correct ID amongst
several potential IDs
● Preload system database with ID photos
of test subjects and several other faces
● Test is a success if the ID of the test
subject is returned by the system
S2 & S4 Test Plan
Edge Link:
Prototype #2 Progress
Component Implementation Completion Date Integration Date
Camera, Face Detection Raspberry Pi 10/2/18 10/2/18
Facial Embedding,
Database ComparisonDesktop Server 10/30/18 11/20/18
Interface Python GUI 11/8/18 11/20/18
Overall Prototyping Progress
Edge Link: *This schedule does not include integration*
Interface UpdateInstead of developing an app with a GUI to display the data, a web application
accessible over the local area network will be used. This ensures a consistent
viewing experience regardless of platform, as long as the device has a web
browser. Security can be maintained by requiring a password for access and a
keeping the router secure.
If the interface is going to be implemented as a local server accessible over
wifi, the data transfer should be minimized to allow for integration with slow
existing wifi routers. New face-profile matches will be pushed from the server
to the client as they are generated.
Redesign for the Browser Interface
Next Steps
● Finish benchmarking
● Bill of Materials
● Complete Test plans based on benchmarking results
● Complete 3rd Prototype
● Receive confirmation about Human Subjects Testing - Heather Foti
○ Complete IRB review Form A if necessary
Questions?