View-invariant Estimation of Height and Stride for Gait Recognition

We present a parametric method to automatically identify people in monocular low-resolution video by estimating the height and stride parameters of their walking gait. Stride parameters (stride length and cadence) are functions of body height, weight, and gender. Previous work has demonstrated effective use of these biometrics for identification and verification of people. In this paper, we show that performance is significantly improved by using height as an additional discriminant feature. Height is estimated by robustly segmenting the person from the background and fitting their apparent height to a time-dependent model. This method is correspondence-free and works with low-resolution images of people. It is also view-invariant, albeit performance is optimal in near fronto-parallel configurations. Identification accuracy is estimated at 47% for fronto-parallel sequences of 41 people, and 65% for non-fronto-parallel sequences of 17 people, compared with 18% and 51%, respectively, when only stride and cadence are used.

[1]  S. Simon.  Gait Analysis, Normal and Pathological Function. , 1993 .

[2]  Mark S. Nixon,et al.  Recognising human and animal movement by symmetry , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[3]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[4]  Hideo Mori,et al.  A method for discriminating of pedestrian based on rhythm , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[5]  Larry S. Davis,et al.  W4S: A real-time system detecting and tracking people in 2 1/2D , 1998, ECCV.

[6]  Mark S. Nixon,et al.  Gait Extraction and Description by Evidence-Gathering , 1999 .

[7]  Mark S. Nixon,et al.  Comparing Different Template Features for Recognizing People by their Gait , 1998, BMVC.

[8]  G. Johansson.  Visual perception of biological motion and a model for its analysis , 1973 .

[9]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1969 .

[10]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[11]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Larry S. Davis,et al.  W4S : A real-time system for detecting and tracking people in 2 D , 1998, eccv 1998.

[13]  M P Murray,et al.  COMPARISON OF FREE AND FAST SPEED WALKING PATTERNS OF NORMAL MEN , 1966, American journal of physical medicine.

[14]  Qiang He,et al.  Individual recognition from periodic activity using hidden Markov models , 2000, Proceedings Workshop on Human Motion.

[15]  Aaron F. Bobick,et al.  Recognition of human body motion using phase space constraints , 1995, Proceedings of IEEE International Conference on Computer Vision.

[16]  Aaron F. Bobick,et al.  Gait recognition using static, activity-specific parameters , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[17]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[18]  Zatsiorky Vm,et al.  Basic kinematics of walking. Step length and step frequency. A review. , 1994 .

[19]  C. Chatfield,et al.  Fourier Analysis of Time Series: An Introduction , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Hiroshi Murase,et al.  Moving object recognition in eigenspace representation: gait analysis and lip reading , 1996, Pattern Recognit. Lett..

[21]  Heinrich Niemann,et al.  Gait Classification with HMMs for Trajectories of Body Parts Extracted by Mixture Densities , 1998, BMVC.

[22]  Larry S. Davis,et al.  EigenGait: Motion-Based Recognition of People Using Image Self-Similarity , 2001, AVBPA.

[23]  Edward H. Adelson,et al.  Analyzing and recognizing walking figures in XYT , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[24]  J. Cutting,et al.  Temporal and spatial factors in gait perception that influence gender recognition , 1978, Perception & psychophysics.

[25]  Peggy Arnell,et al.  The Biomechanics and Motor Control of Human Gait , 1988 .

[26]  Yang Song,et al.  Towards detection of human motion , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[27]  D. Grieve,et al.  The relationships between length of stride, step frequency, time of swing and speed of walking for children and adults. , 1966, Ergonomics.

[28]  Mark S. Nixon,et al.  Extended Model-Based Automatic Gait Recognition of Walking and Running , 2001, AVBPA.

[29]  Larry S. Davis,et al.  Stride and cadence as a biometric in automatic person identification and verification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[30]  James W. Davis,et al.  Visual Categorization of Children and Adult Walking Styles , 2001, AVBPA.

[31]  J. Little,et al.  Recognizing People by Their Gait: The Shape of Motion , 1998 .

[32]  J. Cutting,et al.  Recognizing friends by their walk: Gait perception without familiarity cues , 1977 .