[1] DNA ATM [2] c 2013 Information Processing Society of Japan. Gait motion descriptors. Osaka University 2. Drexel University a)

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1 1,a) 1,b) 2,c) 1,d) Gait motion descriptors Osaka University 2 Drexel University a) higashiyama@am.sanken.osaka-u.ac.jp b) makihara@am.sanken.osaka-u.ac.jp c) kon@drexel.edu d) yagi@am.sanken.osaka-u.ac.jp [1] DNA ATM [2] 2 1

Urtasun [3] Spencer [4] Han Bhanu[5] Gait energy image (GEI) Makihara [6] (Frequency domain

[10] GEnI GEI Masked GEI based on GEnI (MGEI) Wang [11] 4 1 Chrono-gait image (CGI)

motion descriptor (GMD) Iwama [14] 4,000 (GEI FDF GEnI GFI MGEI CGI) (1) (2) (3) (a) GEI

2 Urtasun [3] Spencer [4] Han Bhanu[5] Gait energy image (GEI) Makihara [6] (Frequency domain feature, FDF) Bashir [8] 1 Gait entropy image (GEnI) Lam [9] Gait flow image (GFI) Bashir [10] GEnI GEI Masked GEI based on GEnI (MGEI) Wang [11] 4 1 Chrono-gait image (CGI) Kobayashi Otsu[12] 251 (Cubic higher-order local auto-corelation, CHLAC) Bashir [13] Gait motion descriptor (GMD) Iwama [14] 4,000 (GEI FDF GEnI GFI MGEI CGI) (1) (2) (3) (a) GEI (b) FDF (c) GENI (d) MGEI (e) GFI (f) CGI (g) CHLAC (h) GMD [7] 1 [6] GEI GEI [14] 1(a) GEI 2

3 FDF 1 FDF GEI 0 GEI GEI 1(b) FDF GEnI GEI GEnI GEI 0 GEI GEI 1(c) GEnI MGEI GEI GEnI GEnI GEI MGEI 1(d) GEI GEnI GFI GFI 1(e) GFI CGI 4 1 CGI 1(f) CGI 2.2 t t + n CHLAC CHLAC CHLAC 1(g) CHLAC 2.3 GMD GFI GMD GMD 1(h) GMD GMD P G D x P i R D (i =1,...,N P ) x G j RD (j =1,...,N G ) N P N G P G ( ) P i x P i G j x G j M i,j M i,j = x P i x G j (1) M M i,j 3.2 M =min i,j M i,j (2) 3

4 1 OU-ISIR Gait Database, The Treadmill Dataset Dataset A Dataset B (a) PCA (b) LDA 2 GEI (Linear discriminant analysis, LDA) (Principal component analysis, PCA) (LDA) 2(a) GEI PCA 2 S W S B S W = S B = N class i=1 N class i=1 N i features j=1 (x i,j m i )(x i,j m i ) T (3) N i features(m i m)(m i m) T (4) S W x = λs B x,x 0 (5) N class N features i i x i,j i j m i i m 2(b) GEI LDA 2 PCA ( 2(a)) LDA ( 2(b)) 4. 3 ( ) ( ) 4.1 The OU-ISIR Gait Database, The Treadmill Dataset [15] Dataset A B Dataset A 2km/h 10km/h 1km/h 34 Dataset B DatasetA 2 Dataset B (Vefirication) 1 N (Identification) 1 1 (False rejection rate, FRR) (False acceptance rate, FAR) (Receiver Operating Characteristics, ROC) 4

4(e), (f) (e) (f) 1 4 1 1 (Equal error rate, EER) 1 1 1 N N N (Cumulative matching

5 (a) ROC ( ) (c) ROC ( ) (b) CMC ( ) (d) CMC ( ) 4(a), (b) ROC CMC 4(c), (d) ROC CMC 4(e) (f) 1 GEI FDF GEnI MGEI ( 4(a), (b) ) ( 4(c), (d)) GEI FDF GEnI MGEI GEnI MGEI GEI FDF 4(e), (f) (e) (f) (Equal error rate, EER) N N N (Cumulative matching characteristics, CMC) CMC 3 90% 3 90% N 7(a) 7(b) 1 GFI GMD GEI FDF 5. GEI GMD 5

6 (a) 2 km/h vs. 4 km/h (b) 2 km/h vs. 6 km/h (a) 2 km/h vs. 4 km/h (b) 2 km/h vs. 6 km/h (c)2km/hvs.8km/h (d) 2 km/h vs. 10 km/h (c)2km/hvs.8km/h (d) 2 km/h vs. 10 km/h (e)4km/hvs.6km/h (f) 4 km/h vs. 8 km/h (e)4km/hvs.6km/h (f) 4 km/h vs. 8 km/h (g) 4 km/h vs. 10 km/h (h) 6 km/h vs. 8 km/h (g) 4 km/h vs. 10 km/h (h) 6 km/h vs. 8 km/h (i) 6 km/h vs. 10 km/h (j) 8 km/h vs. 10 km/h 5 ROC (i) 6 km/h vs. 10 km/h (j) 8 km/h vs. 10 km/h 6 CMC 8(a) 8(d) GEI, GMD 8(b) 8(c) 8(e) 8(f) 8(g) 8(h) GEI GMD 2 GMD 6

7 (a) (b) (c) (a) (b) (d) (e) (f) (a) (b) (c) (g) (h) (d) (e) (f) 9 : : : GEI GMD (g) (h) GEI GFI (g) (h) 8 : : : GEI GMD (g) (h) GEI GFI GMD GEI 9(a) 9(d) GEI GMD 9(b) 9(c) 9(e) 9(f) 9(g) 9(h) GMD 2 GEI GMD GEI GMD GEI GMD 6. 7

8 The OU-ISIR Gait Database, The Treadmill Dataset N GEnI MGEI GMD (1) CHLAC GMD (2) ( HumanID Gait Database [16] ) (3) JSPS (S) Vision, pp , October [12] T Kobayashi and N Otsu. Action and simultaneous multiple-person identification using cubic higher-order local auto-correlation. Proc of the 17th International Conference on Pattern Recognition, Vol. 4, pp , August [13] K Bashir, T Xiang, S Gong, and Q Mary. Gait representation using flow fields. Proc. of the British Machine Vision Conference 2009, September [14] H Iwama, M Okumura, Y Makihara, and Y Yagi. The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition. IEEE Trans. on Information Forensics and Security, Vol. 7, pp , October [15] Y. Makihara, H. Mannami, A. Tsuji, M.A. Hossain, K. Sugiura, A. Mori, and Y. Yagi. The ou-isir gait database comprising the treadmill dataset. IPSJ Trans. on Computer Vision and Applications, Vol. 4, pp , Apr [16] S Sarkar, P J Philips, Z Liu, I Robledo, P Grother, and KBowyer.IEEE Transactions on Pattern Analysis and Machine Intelligence. [1] Uidai. [2] How biometrics could change security. http: //news.bbc.co.uk/2/hi/programmes/click_ online/ stm, Oct [3] R. Urtasun and P. Fua. 3D Tracking for Gait Characterization and Recognition. In Automatic Face and Gesture Recognition, Proceedings. Sixth IEEE International Conference on, Vol. 1, pp , May [4] S Nicholas and J Carter. Towards pose invariant gait reconstruction. Proc. of the IEEE International Conference on Image Processing 2005, Vol. 3, pp , September [5] J Han and B Bhanu. Individual recognition using gait energy image. IEEE Trans. on Pattern Analysis and Machine Intelligence, pp , February [6] Y Makihara, R Sagawa, Y Mukaigawa, T Echigo, and Y Yagi. Gait recognition using a view transformation model in the frequency domain. Proc.ofthe9thEuropean Conf. on Computer Vision, pp , May [7] Y Makihara and Y Yagi. Silhouette Extraction based on Iterative Spatio-temporal Local Color Transformation and Graph-Cut Segmentation. Proc. of the 19th Int. Conf. on Pattern Recognition, pp. 1-4, Dec [8] K Bashir, T Xiang, and S Gong. Gait recognition using gait entropy image. in Proc. 3rd Int. Conf. Imaging for Crime Detection and Prevention,, pp. 1 6, December [9] Toby HW Lam, K. H. Cheung, and James NK Liu. Gait flow image: A silhouette-based gait representation for human identification. Pattern recognition, Vol. 44, No. 4, pp , April [10] K Bashir, T Xiang, and S Gong. Gait recognition without subject cooperation. Pattern Recognit. Letters, Vol. 31, No. 13, pp , October [11] C Wang, J Zhang, J Pu, X Yuan, and L Wang. Chronogait image: a novel temporal template for gait recognition. Proc. of the 11th European Conf. on Computer 8

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