The document discusses testing face authentication on mobile applications. It identifies key areas to test such as validating the machine learning algorithm's correctness, testing across different devices and conditions, and automating the face capture flow. It provides examples of data parameters and scenarios to test, approaches to source test data and validate the algorithm, and methods to test the application functionality including using real or virtual devices and automation tools. The goal is to thoroughly test critical areas of quality concerns for the face authentication application.

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Testing Face Authentication
on Mobile
Fathima Harris

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The application
Headless
app
Captures
a face
Server
side
Authentic
ation
Success/failure
The goal: Aid 2 factor authentication by face recognition
Client
side
validation
Human?
Real?
Live?
Database

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Questions!
How do you validate correctness of the algorithm?
How many “faces” do you need?
What would a smoke test be?
How much data do you use? Where do you get it from?
How do you test a headless app?
Which all devices to test it on?
Fraudulent transactions?
Can you even automate this?

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The problem
Identifying scenarios and corresponding data
The correctness of face recognition Machine Learning (ML) algorithm
Overall workability of the app across devices, including live face recognition;
Automation
Security
Performance

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Identifying Scenarios

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Data parameters
Gender, Race, Age etc
Lighting conditions
Resolutions
Various camera devices
Accessories (Glasses, turban, mask, etc)
Aspect ratio
Nearness
Various positions/conditions of the face (different profiles, eyes open/close, smiling etc)
Fake images
Dataset

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Dataset segregation
Training dataset
Testing dataset

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Where do you get this data from?
Open source
Paid sources
Create your own!

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Validating correctness of the ML
algorithm

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Validation criteria
Speed
Accuracy

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Integration of the algorithm with the
app

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Testing the application functionality
Choice of devices: Real/virtual devices
Choice of devices: Cloud services/real device lab
Question: How to test a headless app?
Choice of automation tool
Question: How to automate face capture flow?

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How did the Face authentication flow work?
Camera
launch
Stream of images
Run client side
validation (ML
algorithm)
Valid?
No
Yes
Authentication
from server

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Automating face capture
Launch app
via sample
app
Camera
launch
Client side
validation,
server side etc.
Feed image (s)
from gallery
Stream of images

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Conclusion

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Learnings
Identify critical areas of quality concerns based on the application
Prioritize
Evolve overtime
Do not reinvent the wheel
Automate :)

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Thank you!
Twitter: @Fathima_Harris
LinkedIn: www.linkedin.com/in/fathima-harris