Implementing Biometric Authentication & Features in iOS Apps
1 like502 views
The document discusses the implementation of biometric authentication in iOS apps, detailing various types of biometrics such as facial recognition, fingerprint scanning, and more. It provides a historical overview of biometric technology development from the 1960s to recent advancements like Face ID and Touch ID in iOS. Furthermore, it covers Core ML for machine learning integration into apps, including model conversion and usage in biometric applications.
Implementing Biometric Authentication & Features in iOS Apps
- 1. Implementing Biometric Authentication & Features in iOS Apps Jean-Luc David iOS Developer MyPlanet,Inc
- 2. 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
- 3. Facial Recognition circa 2012
- 5. 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)
- 6. Eigenfaces
- 7. Touch ID • Introduced in iOS 7 • iPhone5s / iPadAir2 / iPadMini3 • Unlock device (Passcode) • iTunes/AppStore purchases • Pay
- 8. iOS 8 • Touch ID Security APIs • Local Authentication • LAContext • secItem • App needs to be foreground
- 10. 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>
- 11. 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.") } } }
- 12. 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.
- 13. iOS 11 Face ID - iPhone X
- 16. Keychain
- 17. Architecture
- 18. Face Recognition with ARKit
- 22. Nomenclature
- 23. Application of Machine Learning
- 24. Silicon Valley - Jian-Yang's Not Hotdog App
- 26. Types of Machine Learning
- 27. 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.
- 28. But I want to train models!
- 29. Runs On Device User Privacy Data Cost Server Cost Available
- 30. How Does a Model Work?
- 31. Machine Learning Model Types
- 32. Demo - Face Detection & Recognition
- 33. Types of Machine Learning Offline + Labels
- 34. Task - Find a Rose
- 37. Core ML Model • Single Document • Public Format • Reduced Size • Improved Accuracy • Decreased Prediction Time
- 38. Core ML Architecture • Unified Fine Tuned Inference Engine • Xcode Integration • Built on Accelerate and Metal • Public Model Format • Support for Popular Training Libraries
- 39. Core ML Model • Function learned from data • Observed inputs • Predicts outputs • Single Document • Public Format • Ready to Use • Task Specific
- 41. Converting to Core ML Core ML Tools Open Source
- 42. 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
- 43. Demo - Converting a Model to MLModel
- 45. Swift ML Code Generates this code
- 46. Model as Code • Quick Initialization • Optimized Prediction Xcode Your App
- 48. Resources • https://www.slideshare.net/jldavid • https://github.com/jldavid/FaceDetection • https://github.com/jldavid/FlowerClassifier
- 49. Thank You