Phase III Gate Reviewedge.rit.edu/edge/P19591/public/Phase III Gate Review/Phase III Desi… ·...

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?