We want variance within subject << variance between subjects.
1994, idea on gait. 1999 first book on biometrics and supported by DARPA. 2005 first book on gait.
Gait is non-contact and uses sequences.
Advantages: perceivable at distance and hard to disguise.
Potential applications: security/surveillance (监视), immigration, forensics, medicine
Other application: Moving objects
Related fields: animation, tracking
As a biometric, gait is available at a distance when other biometrics are obscured or at too low resolution.
Many medical studies concern pathological (病态的) gait.
Point markers attached to a subject.
Can use video, optoelectronics (光电子学) moving light displays, electrogoniometers (电子侧向仪).
Use image frame extract subject.
6 subjects; 7 sequences. Use Sony camera. Walk in circular track.
Acquired by NIST on DARPA human ID at a Distance program. 122 subjects. 30 fps video.
Include change in surface, shoe and luggage. Different Views, Different surfaces.
Filmed indoors and outdoors.
Included covariate data for 12 subjects.
124 subjects, 11 viewpoints.
From Osaka Univ, Japan. Gathered large database > 1000 subjects, at exhibition.
Consistent with many other studies. First gait biometrics paper had 90% CCR.
IdentificationRate to Rank;
False Rejection Rate to False Acceptance Rate.
- Silhouette description (Many)
- Established statistical analysis
- Temporal symmetry
- Velocity moments
- Extension of spatial moments
- Applied to silhouettes
- Selected by ANOVA
- 3 moments for visualisation; subjects are clusters of 4
- Unwrapped silhouette
- Average Silhouette
- Most popular technique for gait representation
- Simple and effective
- Also called gait energy image
- New form is gait entropy image
- Background is taken from each frame and pixels thresholded resulting in a binary image
- Normalise silhouettes by height to account for distance
- Add all silhouettes together and divide by the number of frames
- Resulting image is the signature
- HiD Baseline Analysis
- Form silhouette (background subtraction)
- Detect gait periods
- Estimate correlation of frame similarity between sequences
- Similarity is median of max correlation between gallery and probe sequences
- HMM Analysis
- Form silhouette
- Use contour width
- Captures structure and dynamics
- Capture gait information using Hidden Markov Model
- Finding moving objects
- Describe shape by Fourier descriptor
- Include velocity in accumulation
- Extract moving continuous shape
- Include trajectory described by Fourier descriptor
- Allow for arbitrary deformation
- Modelling Movement (few)
- Pendular thigh motion model
Modeling the Thigh’s Motion:
Extended pendular thigh-model, based on angles
Uses forced oscillator (振荡器)/bilateral symmetry/phase coupling (双边对称/相位联轴器)
- Translation to the real world
- Finding subjects in outdoor imagery: invariance to background etc.
- Analysing covariate structure: invariance to factors which affect gait
- Understanding feature space: invariance to recognition methodology
- Analysing other views: invariance to viewpoint
- Forensic analysis: can criminals be recognised
- Anatomically-guided skeleton
- 3D Recognition – Marionette Based
- Analysing the Effects of Time
- Pendular thigh motion model