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Implementing Biometric Authentication 

& Features in iOS Apps
Jean-Luc David
iOS Developer
MyPlanet,Inc
What are Biometrics?
• A biometric is a unique, measurable characteristic of a human being
that can be used to automatically recognize an individual or verify an
individual‟s identity. Biometrics can measure both physiological and
behavioral characteristics. Physiological biometrics (based on
measurements and data derived from direct measurement of a part of
the human body) include:

• Finger-scan
• Facial Recognition
• Iris-scan
• Retina-scan
• Hand-scan
Facial Recognition circa 2012
Open Computer Vision
History of Biometrics (1960-2017)
• Woodrow Wilson Bledsoe - Manual Measurements (1960) Rand tablet
• Goldstein, Harmon, and Lesk - 21 Facial Markers (1970)
• Sirovich and Kirby - EigenFaces (1998) Low-dimensional representation of facial
images.
• Turk and Pentland - Face Recognition in images (1991)
• DARPA - FacE REcognition Technology (FERET) (1993-2000)
• National Institute of Standards and Technology (NIST) - Face Recognition Vendor
Tests (FRVT) (2000)
• Social Media (2000)
• Pinellas County Sherriff’s Office - Law Enforcement Forensic DB (2009)
• Panama Airport - FaceFirst’s facial recognition platform (2011)
• U.S. Military - Osama Bin Laden Identified (2011)
• Automated Regional Justice Information System (ARJIS) (2014)
• Retail Loss Prevention (2017)
• iPhone X (2017)
• FaceFirst Watchlist as a Service (2017)
Eigenfaces
Touch ID
• Introduced in iOS 7
• iPhone5s / iPadAir2 / iPadMini3
• Unlock device (Passcode)
• iTunes/AppStore purchases
• Pay
iOS 8
• Touch ID Security APIs
• Local Authentication
• LAContext
• secItem
• App needs to be foreground
Implementing Biometric Authentication & Features in iOS Apps
Info.plist - NSFaceIDUsageDescription
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
"http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>NSFaceIDUsageDescription</key>
<string>This app requires Face ID.</string>
</dict>
</plist>
Detecting Biometric Capabilities
import LocalAuthentication
let context = LAContext()
var error: NSError?
if context.canEvaluatePolicy(.deviceOwnerAuthenticationWithBiometrics,
error: &error) {
if #available(iOS 11.0, *) {
if context.biometryType == LABiometryType.faceID {
print("Device supports Face ID.")
} else if context.biometryType == LABiometryType.touchID {
print("Device supports Touch ID.")
} else {
print("Device does not support Biometrics.")
}
}
}
Detecting Biometry Changes
import LocalAuthentication
let context = LAContext()
var error: NSError?
if context.canEvaluatePolicy(.deviceOwnerAuthenticationWithBiometrics,
error: nil) {
if let domainState = context.evaluatedPolicyDomainState {
let bData = domainState.base64EncodedData()
if let decodedString = String(data: bData, encoding: .utf8) {
print("Decoded Value: (decodedString)")
}
}
}
}
• If the biometric databases changes significantly (like upgrading to an
iPhone X), the evaluatedPolicyDomainState will also change.
iOS 11 Face ID - iPhone X
Implementing Biometric Authentication & Features in iOS Apps
Implementing Biometric Authentication & Features in iOS Apps
Keychain
Architecture
Face Recognition with ARKit
Popular Perceptions
Popular Perceptions
Popular Perceptions
Nomenclature
Application of Machine Learning
Silicon Valley - Jian-Yang's Not Hotdog App
Fiction Becomes Reality
Types of Machine Learning
Core ML
• Announced at WWDC 17.
• Works only on iOS 11
• Inference engine
• Does not support training models.
• Heterogenous compute architecture - CPU & GPU.
• Open Source Python coremltool for converting
to coremlmodel file.
But I want to train models!
Runs On Device
User Privacy Data Cost Server Cost Available
How Does a Model Work?
Machine Learning Model Types
Demo - Face Detection & Recognition
Types of Machine Learning
Offline
+ Labels
Task - Find a Rose
Inference is Difficult
Inference is Difficult
Core ML Model
• Single Document
• Public Format
• Reduced Size
• Improved Accuracy
• Decreased Prediction

Time
Core ML Architecture
• Unified Fine Tuned Inference Engine
• Xcode Integration
• Built on Accelerate and Metal
• Public Model Format
• Support for Popular Training Libraries
Core ML Model
• Function learned from data
• Observed inputs
• Predicts outputs
• Single Document
• Public Format
• Ready to Use
• Task Specific
Sample Models
https://developer.apple.com/machine-learning/
Converting to Core ML
Core ML 

Tools
Open Source
Converting to Core ML
• Download a trained .caffemodel and
a .prototxt of the dataset, as well as a .txt list
of names related to the model.
• Install Python 2.7 and pip
• Install, create, then activate the virtualenv.
• Write and run a Python script to convert
the .caffemodel to a Core ML model,
using coremltools
• Add the generated .mlmodel to the Xcode project
Demo - Converting a Model to MLModel
Xcode Integration
Swift ML Code
Generates this code
Model as Code
• Quick Initialization
• Optimized Prediction
Xcode Your App
Demo - FlowerClassifier
Resources
• https://www.slideshare.net/jldavid
• https://github.com/jldavid/FaceDetection
• https://github.com/jldavid/FlowerClassifier
Thank You
Questions?

jldavid@gmail.com

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