HID DARPA Niyogi Adelson [5] Cunado [6] 1D. Little Boyd [7] Murase Sakai [8] Huang [9] Shutler [10] TP391 1M02J , [1] [3]

Transcription

1 * 8 D D TP39 [] [3] DARPA HID [] 6 [4] [5-7] iyogi Adelson [5] Cunado [6] D Little Boyd [7] Murase Sakai [8] Huang [9] Shutler [] * 69855, 65 MJ4.

2 Hayfron-Acquah [] Johnson Bobick [] Yam [7] D PCA LPR. LMedS (Least Median of Squares) [8] I B xy B xy t xy = min med ( I q) () q t q x, y [9] ( a + )( b + ) (56 a)( 56 b) f ( a,b) = ( a + ) + ( b + ) (56 a) + (56 b) () f ( a,b) < a( x, y),b( x, y) 55 a (x, y) b (x, y) x, y I xy 3 []. [] 3

3 x c = b b i= x i, y c = b b i= (x c, y c ) b (x i, y i ) y i (3) d i ( xi xc ) + ( yi yc = ) (4) 3 L- 3.3 s D i,j i j i i t = s [D,, D,,, D,, D,,, D s,s ] s i m d = Di, j (5) t i= j= = t s i i= j= ( D i, j m d )( D i, j m d ) T (6)? λ, λ,, λ e, e,, e T s k< [e, e,, e k ] D i,j P i,j T i, j [ e e... ek Di,j P = ] (7) k i C i = Pi, j (8) i j= 3

4 3 3. STC Spatio-temporal Correlation I (t) I (t) P (t) P (t) T T = [ e e... e ] I ( ), P t) [ e e... e ] I ( ) P ( t) k t ( k t T = (9) d = min P ( t) P ' ( at + b) () ab t = P (at+b) P (t) a b f f a=f /f b P (t) f a b P (t) ( ) / (.5 ) ( 3 ) ( 4 ) ( 5 ) 4 ED ormalized Euclidean Distance P (t) P (t) C C C ( ), = P t C = P ( t) () t = t = C C d = () C C 5 5 d Exemplar Projection Centroid

5 3. ( the earest eighbor Classifier) (E the earest eighbor Classifier with respect to Exemplar) E LPR Panasonic V-DXE (a) (b) (c) D D 6 8~35 PCA % e Leave-one-out 5 LPR e -. Cross Validation e degree 45 degree 9 degree 5

6 8 CCR Correct Classification Rate e3 e e e e -7 4 e e e e 5 6 (a) (b) (c) STC ED 8 LPR º 8 CCR 45º 8 9º 8 65.% 63.75% 77.5% 65.% 66.5% 85.% E 75.% 8.5% 93.75% ROS Rank Order Statistic FERET k p [] (k) Cumulative Match Scores k [] ROS 9(a) (b) STC ED 9(a) STC 9(b) ED p () Rank= Cumulative Match Score Degree 45 Degree 9 Degree 5 Rank 5 Cumulative Match Score Degree 45 Degree 9 Degree Degree 45 Degree 9 Degree 5 Rank 5 (a) FAR (False Acceptance Rate) FRR (False Reject Rate) 8 9 ED ROC (Receiver Operating Characteristic), 45, 9 EER (Equal Error Rate) 9%, 3%, % 6 (b) 9 FERET

7 9 ) ED STC STC 45 Degree.8 9 Degree.6 EER ) ED ED ED ROC FAR.4. 3) Degree..4 FRR SOTO SOTO [6, 9,,, 3, 7] SOTO SOTO [] [ [3] [4] SOTO LPR 7.5%( ) [] 4 (SOTO) 93.75% [] 4 (SOTO).% [3] 6 4 (SOTO) 83.% 7 4 (SOTO) 96.43% [4] 4 (LPR º) 7.5% 4 (LPR º) 75.% 7

8 [7, 9, ] [8] [4] 4 7 /4 [5-4, 6, 7] [5] 8 /6 73% 3 [5-, 3-7] 5 3D Wang L, Hu W, Tan T. Recent developments in human motion analysis. Pattern Recognition (accepted) 3 Jain A, Bolle R, Pankanti S. Biometrics: personal identification in networked society. Kluwer Academic Publishers, Boston, ixon M, Carter J, Cunado D, Huang P, Stevenage S. Automatic gait recognition. In: BIOMETRICS Personal Identification in etworked Society, Kluwer Academic Publishers, Boston, iyogi S, Adelson E. Analyzing and recognizing walking figures in XYT. In: Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, USA, Cunado D, ixon M, Carter J. Using gait as a biometric, via phase-weighted magnitude spectra. In: Proc International Conference on Audio- and Video-based Biometric Person Authentication, Crans-Montana, Switzerland, Little J, Boyd J. Recognizing people by their gait: the shape of motion. Journal of Computer Vision Research, 998, (): -3 8 Murase H, Sakai R. Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letters, 996, 7: Huang P, Harris C, ixon M. Human gait recognition in canonical space using temporal templates. Vision Image and Signal Processing, 999, 46 (): 93- Shutler J, ixon M, Harris C. Statistical gait recognition via temporal moments. In: Proc IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, Texas, Hayfron-Acquah J, ixon M, Carter J. Automatic gait recognition by symmetry analysis. In: Proc International Conference on Audio- and Video-based Biometric Person Authentication, Halmstad, Sweden, Johnson A, Bobick A. A multi-view method for gait recognition using static body parameters. In Proc International Conference on Audio- and Video-based Biometric Person Authentication, Halmstad, Sweden,

9 3 Foster J, ixon M, Prugel-Bennett A. ew area based metrics for gait recognition. In: Proc International Conference on Audio- and Video-based Biometric Person Authentication, Halmstad, Sweden, BenAbdelkader C et al. EigenGait: motion-based recognition of people using image self-similarity. In: Proc International Conference on Audio- and Video-based Biometric Person Authentication, Halmstad, Sweden, Tanawongsuwan R, Bobick A. Gait recognition from rime-normalized joint-angle trajectories in the walking plane. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Hawaii,. II: Shakhnarovich G, Lee L, Darrell T. Integrated face and gait recognition from multiple views. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, Hawaii,. I: Yam C, ixon M, Carter J. Gait recognition by walking and running: a model-based approach. In: Proc Asia Conference on Computer Vision, Melbourne, Australian,. I: -6 8 Yang Y, Levine M. The background primal sketch: an approach for tracking moving objects. Machine Vision and Applications, 99, 5: Kuno Y et al. Automated detection of human for visual surveillance system. In: Proc International Conference on Pattern Recognition, Vienna, Fujiyoshi H, Lipton A. Real-time human motion analysis by image skeletonization. In: Proc IEEE Workshop on Applications of Computer Vision, Princeton, J, Haritaoglu I, Harwood D, Davis L. W 4 : real-time surveillance of people and their activities. IEEE Trans Pattern Analysis and Machine Intelligence,, (8): Phillips J, Moon H, Rizvi S, Rause P. The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Analysis and Machine Intelligence,, (): 9-4 Gait-based Human Identification WAG Liang, HU Wei-Ming, TA Tie-iu (ational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 8) Abstract Human identification at a distance has recently gained more and more interests from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. Gait is a particularly attractive modality from a surveillance perspective. In this paper, a simple but efficient motion-based gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, an improved background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure from the background. Then, eigenspace transformation based on the traditional Principal Component Analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait, especially biometric shape cues. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively lower computational cost. Keywords biometrics, gait recognition, background subtraction, PCA, spatio-temporal correlation 9

10 977 9 WAG Liang, male, born in 977. He received his B. Sc. (997) and M. Sc. () in the Department of Electronics Engineering and Information Science from Anhui University, China. He is currently a Ph. D. candidate in the ational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. He has published more than papers on major national journals and international conferences. His research interests include pattern recognition computer vision, digital image processing and analysis, visual surveillance, etc. 968 HU Wei-Ming, male, born in 968. He received his Ph. D. Degree from the Department of Computer Science and Engineering, Zhejiang University, China. From April 998 to March, he worked as a Postdoctoral Research Fellow at the Institute of Computer Science and Technology, Founder Research and Design Center, Peking University. From April, he worked at the ational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, as an Associate Professor. He has published more than papers on major national journals, such as Science in China, Chinese Journal of Computers and Chinese Journal of Semiconductors. His research interests include visual surveillance in dynamic scenes, neural network, image processing, 3D computer graphics, etc. 964 TA Tie-iu, male, born in 964. He received his B. Sc. (984) in electronic engineering from Xi'an Jiaotong University, China, and M. Sc. (986), DIC (986) and Ph. D. (989) in electronic engineering from Imperial College of Science, Technology and Medicine, London, U. K. In October 989, he joined the Department of Computer Science, The University of Reading, England, where he worked as Research Fellow, Senior Research Fellow and Lecturer. In January 998, he returned to China to join the ational Laboratory of Pattern Recognition, the Institute of Automation of the Chinese Academy of Sciences, Beijing, China. He is currently Professor and Director of the ational Laboratory of Pattern Recognition and Director of the Institute of Automation. He is a Senior Member of the IEEE and was an elected member of the Executive Committee of the British Machine Vision Association and Society for Pattern Recognition ( ). He serves as referee for many major national and international journals and conferences. He is an Associate Editor of the International Journal of Pattern Recognition, the Asia Editor of the International Journal of Image and Vision Computing and is a founding co-chair of the IEEE International Workshop on Visual Surveillance. His current research interests include speech and image processing, machine and computer vision, pattern recognition, and robotics lwang@nlpr.ia.ac.cn