Combating Accuracy Issues in Biometric Solutions

Transcription

1 NEC Smart City Forum (KL) Combating Accuracy Issues in Biometric Solutions Christopher Lam Deputy Head and Senior Director NEC Laboratories Singapore (NLS) 1 NEC Corporation 2018

2 Biometric Authentication Technology 2 NEC Corporation 2018

3 NEC s suite of Biometrics Solutions Bio-IDiom Contactless Contact-based Fingerprint/ Palmprint Finger vein Ear Acoustics Authentication Iris Face Voice 3 NEC Corporation 2018

4 Characteristics of Different Biometric Authentication Fingerprint recognition offers great convenience with high accuracy and ease of use. Face recognition is highly convenient in that it does not require any special actions Walk-through entry/exit while holding your luggage, etc. High DNA Iris Vein Finger print Contact-based Contactless Accuracy Palm print Otoacoustic Face Low Low 4 NEC Corporation 2018 Hand writing Convenience Voice NEC s key technology High *Source: Addition to Biometrics Market White Paper 2007 version Yano Research Institute, Ltd.

5 Facial Authentication in Biometrics Authentication Facial authentication is an authentication method similar to how a person identifies another person な認証方式 1. Natural Feeling We usually recognize person by face. Less resistance for user 3. Ease of Verification It's possible for a human operator to verify the correctness by comparing the photos. 2. No Special Action No special action like touching devices, at the most look at the camera. Convenience 4. No special devices No special devices, just a camera (with or without infrared). Easy for a human to verify Even webcam can be used 5 NEC Corporation 2018

6 History of NEC s Face Technology 1963 Started character recognition technology R&D Started face recognition R&D Application of pattern recognition technology established through character recognition R&D Commercialized face recognition SDK NeoFace Ranked No.1 in NIST *1 Evaluation 3 consecutive wins in Still Image Matching 2017 NIST FIVE No Character R&D 2009(MBGC *2 ), 2010(MBE *3 ), 2013(FRVT *4 ) Newly ranked No.1 in Video Face Evaluation NIST Face in Video Evaluation (FIVE *5 ) Evaluation under various environmental conditions 1989 Face R&D NIST Evaluation 3 Consecutive Wins Still Image Matching Video Face *1 US Institute of Standards and Technology *2 MBGC (Multiple Biometric Grand Challenge) *3 MBE(Multiple Biometrics Evaluation) *4 FRVT(Face Vendor Test) *5 FIVE (Face In Video Evaluation) 6 NEC Corporation 2018

7 Facial 1-to-1 Matching Are You who You said You are? 2 1) Input Identity 2) Input Picture Facial System Matching Score (0.89) =? > Threshold (0.85) 1 ID/Face Database Photo used during Enrollment 7 NEC Corporation 2018

8 Facial 1-to-N Matching Who are You? Facial System Distant Top from the rest =? Input Picture Face/ID Database 8 NEC Corporation 2018

9 Facial N-to-N Matching Who is present at the Scene? Distant Top from the rest Facial System =? Video Frames Face/ID Database 9 NEC Corporation 2018

10 Difference from Still Image Matching Video Face is far more difficult OK OK OK High Speed (real time) Simultaneous recognition Distance from camera (low resolution) Various face angles Various lighting OK OK OK Still Image Cooperative Cooperative Video Non-Cooperative 10 NEC Corporation 2018

11 Different Modes of Operation for Facial Modes of Operation Accuracy Measurement 1-1 Matching No. of times, the system accurately agrees that the subject is who he/she says he/she is 1-N Matching N-N Matching No. of times, the system accurately points out that the subject is the correct person registered in the database No. of times the system accurately flags out that the correct registered persons are present (There can be missed detections also) Among the above modes, the first one is the simplest and has the highest chance of achieving high accuracy because of the 2 nd factor But identical twins may still fool the system 11 NEC Corporation 2018

12 Garbage-In-Garbage-Out (GIGO) Any System 12 NEC Corporation 2018

13 Factors contributing to the accuracy of Facial Cooperativeness of Subjects Quality of Enrollment Photo Camera Specs & Settings Quality of Input Image/Video Camera Positions & Lighting Factors contributing to the Accuracy of Facial Size of Database Facial Algorithm 15 NEC Corporation 2018

14 Compensate for the High Accuracy Facial System, How? Compensate for the non-cooperativeness non-cooperativeness of Subjects of Subjects Suitable cameras and proper settings Use Good Quality Photo for Enrollment (where possible) Capture Good Quality Face Images How to implement a High Accuracy Facial System? Use proven FR technologies like NEC s NeoFace Suitable camera positions & lighting to capture faces at good angles Limit size of Database (where possible) 16 NEC Corporation 2018

15 Multi-modal Biometrics NEC の生体認証 Iris Face Finger vein Fingerprint/ Palmprint Voice Ear acoustic 17 NEC Corporation 2018

16 Other ways to identify a Person from a Distance Head Person Head/ Silhouette Face (Shape) Head Upper Body Upper Clothing Body Color MAC Address Lower Body Lower Clothing Body Color Lower Body Walking Clothing Style Color 18 NEC Corporation 2018

17 NLS on LinkedIn Follow us on LinkedIn and keep up to date with the news, articles and opportunities available 20 NEC Corporation 2018