Gait-based Person Authentication by Wearable Cameras

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1 Gait-based Person Authentication by Wearable Cameras INSS2012, Antwerp, June 2012 Presenter: Kohei Shiraga Coauthors: Ngo Thanh Trung Ikuhisa Mitsugami Yasuhiro Mukaigawa Yasushi Yagi The Institute of Scientific and Industrial Research, Osaka Univ. 1

2 Background Surveillance Cameras Stores, banks and stations, etc. Useful for crime-prevention Problem: blind spots back alley A new technique for crime-prevention is required 2

3 Wearable surveillance (WS) A new concept for crime-prevention Use WS system WS system observes & analyzes environment Sensor Surveillance Danger! Alert Compact Computer 3

4 WS system WS systems must: Be compact and lightweight Be Stand-alone Required functions User authentication Scene understanding Notification Stolen Chased by 4

5 Our contributions A prototype system User Authentication for WS systems The wearer == The owner?? Gait-based method Implement on the prototype =3 5

6 Prototype Stereo camera pair FFMV-03MTC x2 (Point Grey Research) Compact computer NXV PCB (Fujitsu Kyushu Network Technologies) Battery XP8000 (XPAL Power) Has an image processor Real-time image processingz 600g weight (except for the school bag) 6

7 Gait-based user authentication for WS systems Utilize wearable cameras Gait signal estimated from continuous images Authenticate the user based on such signal Authentication as well as observation images Motion estimation Authentication result Image processing Scene understanding 7

8 Proposed authentication method Outline Gallery (template data) Motion estimation Comparison Decision Image sequence Motion signal 8

9 Motion estimation Real-time robust motion estimation[trung et al.] Raw image Feature point detection Classifying into far/near far near RANSAC-based estimation Using only far points Motion Signal (Rotation) 9

10 Motion estimation on prototype Apply hardware acceleration Raw image Feature point detection Classifying into far/near far near RANSAC-based estimation Using only far points Motion Signal (Rotation) 10

11 Motion estimation on prototype Apply hardware acceleration Raw image Feature point detection Classifying into far/near far near RANSAC-based estimation Using only far points Utilize the image processor Motion Signal (Rotation) 11

12 Motion estimation on prototype Processing time Time required for estimation between f t-1 and f t Feature point detection Far/near classification RANSAC-based estimation Total Duration [ms] Achieved real-time estimation for 30fps input stream 12

13 Comparison with gallery Period-based matching Self-DTW (Self Dynamic Time Warping) [Makihara et al.] Period detection DTW-based matching [Trung et al.] Computing distance Comparing with threshold Gallery Decision 13

14 Experiment Settings Using our prototype system 39 adult subjects Outdoor Natural walking speed 30 seconds 4 sequences one sequence for gallery three sequences for input 14

15 Results FRR [%] ROC curve BAD EER = 5.6% GOOD FAR [%] ROC curve: describes the trade-off between FAR and FRR Estimate Ground Truth Genuine Genuine TAR FAR Imposter FRR TRR FAR: False Acceptance Rate FRR: False Rejection Rate EER: The error rate where FAR = FRR Imposter 15

16 Results EERs comparable to the accuracy of existing methods using motion sensors # of subjects Sensor type EER [%] Ailisto et al. 36 accelerometer 6.4 Gafurov et al. 21 accelerometer 5-9 Mantyjarvi et al. 36 accelerometer 7-19 Trung et al. 32 accelerometer 6.0 Our method 39 camera

17 Conclusion Prototype system Using a hardware specialized for image processing A novel user authentication method for WS systems Utilizing motion estimated from camera images Evaluated with real data from 39 subjects The accuracy of our method is comparable to those of existing methods 17

18 Future work Further development Sophisticate our authentication method Other functions for the WS system Field test approaching Here! Condition: OK Send data via wireless network User (a child) Parents 18

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