Computer Security Symposium October ,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji U

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1 Computer Security Symposium October 017 1,a) 1,b) Microsoft Kinect Kinect, Takafumi Mori 1,a) Hiroaki Kikuchi 1,b) [1] 1 Meiji University Graduate School of Advanced Mathematical Science a) cs1705@meiji.ac.jp b) kikn@meiji.ac.jp Microsoft Kinect v EER

2 1 m : n 1 : n n 1 : n m n m n m : n n 1 3. Han 006 (GEI: Gait Energy Image)[3] 1 1 Kinect v RGB RGB 30fps fps m Shiraga GEINet[1] (CNN: Convolutional Neural Network) GEI 015 Anderson Kinect [4] 013 Igual [5] Kinect v Kinect v Microsoft NUI(Natural User Interface) [8] Kinect RGB 3 Kinect SDK Kinect v [7] Kinect v

A B C 1 シークエンス D E 4 165cm 170cm 0 51 3 1 4.

1 1 3 1 A 3 E 1 S S4 S1 S0 S1 S18 S3 S17 S11

3 .3m 1m Kinect.m 1 Kinect v [6] 3 017/08/05 017/08/ D A B C 1 シークエンス D E 4 165cm 170cm U 1 U 10 ID A 3 E 1 S S4 S1 S0 S1 S18 S3 S17 S11 S16 S15 S14 S13 S1 S10 S S6 S5 S8 S7 S S4 S3 S

4 5 ID S 1 FootL-AnkleL S FootR-AnkleR S 3 AnkleL-KneeL S 4 AnkleR-KneeR S 5 KneeL-HipL S 6 KneeR-HipR S 7 HipL-SpineBase S 8 HipR-SpineBase S SpineBase-SpineMid S 10 SpineMid-SpineShoulder S 11 SpineShoulder-ShoulderL S 1 SpineShoulder-ShoulderR S 13 ShoulderL-ElbowL S 14 ShoulderR-ElbowR S 15 ElbowL-WristL S 16 ElbowR-WristR S 17 WristL-HandL S 18 WristR-HandR S1 HandL-HandTipL S 0 HandR-HandTipR S 1 WristL-ThumbL S WristR-ThumbR S 3 SpineShoulder-Neck S 4 Neck-Head D6 A A1 D5 D4 A6 A5 D3 D D1 A4 A ( ) i k f {S 1,..., S 4, D 1,..., D 6, A 1,..., A 6 } µ(f i,k ), median(f i,k ), max(f i,k )

5 6 ID D 1 FootL-FootR D AnkleL-AnkleRigh D 3 KneeL-KneeR D 4 FootL-HandTipL D 5 FootR-HandTipR D 6 HandTipL-HandTipR ID A 1 ShoulderL-HandTipL A ShoulderR-HandTipR A 3 HipL-AnkeL A 4 HipR-AnkleR A 5 HipL-KneeL A 6 HipR-KneeR θ µ(f i,k ) µ(f j,k ) T same(i, j) = F if µ(f i,k ) µ(f j,k ) θ otherwise f g T if (µ(f i,k ) µ(f j,k )) + (µ(g i,k ) µ(g j,k )) θ same(i, j) = F otherwise Y Head HandTipLeft HandTipRight AnkleLeft AnkleRight X D 5 8 U 6 D 5 Median µ(d 5 ) µ(d 5 ) Distance D D(MovingAverage) Frame 8 1 D

6 8 ID µ(d 5 ) median(d 5 ) max(d 5 ) U U U U U U Value Value mean median max U 6 D 5 U7 U5 U1 U8 U U10 U U6 U3 U4 UserID 10 µ(d 5 ) 11 (FAR) (FRR) (ROC) 1 µ(s 6 ),median(d 1 ), max(a ) 3 3 median(s 6 ) Density FAR Self Others Distance 11 µ(d 5 ) ) FRR S6 mean D1 median A max µ(s 6 ),median(d 1 ), max(a ) ROC EER Top10 EER EER µ(d 5 ) 0. max(d 5 ) 0. µ(s 6 ) 0.30 median(d 5 ) 0.30 µ(s 5 ) 0.31 median(a 4 ) 0.31 median(s5) 0.31 median(s 6 ) 0.31 µ(d 4 ) 0.3 µ(a 4 ) EER

7 Value Num of Feature EER 15 Max MDS 14 Density Self Others Distance (Max) 6 5. EER Max EER 13 EER 5 Max A1 A6 (MDS: Multi-Dimensional Scaling) Kinect Kinect v A 5,A 6 EER 7. Kinect v EER

8 10 EER 10 EER µ(d 3 ), µ(d 5 ) 0.5 µ(d 3 ), µ(d 4 ), µ(d 5 ) 0.5 µ(d ), µ(d 3 ), µ(d 4 ), µ(d 5 ) 0.5 µ(d 3 ), µ(d 4 ), µ(d 5 ), µ(d 6 ) 0.5 µ(d 1 ), µ(d 3 ), µ(d 4 ), µ(d 5 ), µ(d 6 ) 0.5 µ(d ), µ(d 3 ), µ(d 4 ), µ(d 5 ), µ(d 6 ) 0.5 µ(d ), µ(d 3 ), µ(d 5 ) 0.6 µ(d 1 ), µ(d ), µ(d 3 ), µ(d 4 ), µ(d 5 ) 0.6 µ(d 4 ), µ(d 5 ), µ(d 6 ) 0.6 µ(d 1 ), µ(d ), µ(d 3 ), µ(d 4 ), µ(d 5 ), µ(d 6 ) EER 10 EER µ(a 6 ) 0.50 median(a 6 ) 0.48 median(a 5 ), µ(a 6 ) 0.47 median(a ), 0.47 median(a ), median(a 5 ) 0.46 max(a 5 ) 0.45 max(a 5 ), 0.45 median(a ), median(a 5 ), median(a 6 ) 0.45 median(a ), median(a 6 ) 0.45 median(d 1 ) 0.45 ( ) [8],, SCIS015, Jan 015. [1] K. Shiraga, Y. Makihara, D. Muramatsu, T. Echigo, Y. Yagi, GEINet: View-Invariant Gait Recognition Using a Convolutional Neural Network, In Proc. of the 8th IAPR Int. Conf. on Biometrics (ICB 016), pp.1-8, Halmstad, Sweden, Jun 016 [] S. D. Bakchy, M. R. Islam, A. Sayeed, Human identification on the basis of gait analysis using Kohonen selforganizing mapping technique nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), Dec 016 [3] J. Han and B. Bhanu, Individual recognition using gait energy image, IEEE Trans. Pattern Anal. Mach. Intell., vol.8, no., pp.3163, 006. [4] V. Andersson and R. Araujo, Person Identification Using Anthropometric and Gait Data from Kinect Sensor, In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence [5] L. Igual, À. Lapedriza, R. Borràs, Robust gait-based gender classification using depth cameras, EURASIP Journal on Image and Video Processing 013, 013. [6] JointType Enumeration - MSDN - Microsoft, ( 8 ) [7] Kinect - Microsoft Developer, - 7 -

Computer Security Symposium October 2018 DTW 1 2 Microsoft Kinect 3 DTW EER EER 5 45 Kinect DTW 1. [1] Muaaz [5] DTW [2][3] [2] 2 10

Computer Security Symposium October 2018 DTW 1 2 Microsoft Kinect 3 DTW EER EER 5 45 Kinect DTW 1. [1] Muaaz [5] DTW [2][3] [2] 2 10 Computer Security Symposium 2018 22-25 October 2018 DTW 1 2 Microsoft Kinect 3 DTW 6 145 5 45 Kinect DTW 1. [1] Muaaz [5] DTW [2][3] [2] 2 10 4 ( ) 0.25 1 Graduate School of Advanced Mathematical Sciences,