1. Gait Recognition
Gökhan ŞENGÜL

2. What is Gait recognition
It aims to discriminate individuals by the way they walk
It is unobtrusive.
It depends on how the silhouette shape of an individual changes over time in an image sequence

3. Gait recognition Why use gait as a biometric? Non-invasive
Acqusition of data from a distance location
Recognizing individuals in unconstrained enviroments with uncooperative subjects
Process sequence of images
More information than other biometrics
Greater robustness/reliability

4. Gait recognition Capture subject’s gait
Video ideally with chroma-key background
Avoid occlusion of subject
Outdoor images cause some problems
Process video of subject walking
Background subtraction
Indoor – Chroma-key, Outdoor – Mixture of Gaussians
Binary silhouette of each frame
~30 frames captures complete gait cycle
Begin at known heel-strike

5. Gait recognition Advantages
Data can be acquired at large stand-off distances (the same is also true for face recognition)
Low spatial resolution (degrade accuracy)
Distance based identification
Passive
Human gait has been observed to have some person-specific characteristics
Can be used to recognize the gender

6. Gait recognition Advantages
Data can be acquired at large stand-off distances (the same is also true for face recognition)
Low spatial resolution (degrade accuracy)
Explicit subject interaction is not required
Features can be extracted from low resolution images

7. Symmetry Crop and resize images to 64x64 Centre the body in the image
Extract symmetry for each image in sequence
Average all symmetry maps to get Gait Signature
Compare Gait signatures directly + + + =
Number of images

8. Gait Recognition Algorithm

9. Human Detection and Tracking

10. FEATURE EXTRACTION Silhouette Representation
Typically, an algorithm for gait recognition begins with a silhouette extraction process. This component aims to isolate (i.e., segment or localize) the contour of the human body from a video sequence. A simple method for accomplishing this is through background subtraction, on a frame-by-frame basis,

11. FEATURE EXTRACTION Silhouette Representation
2D silhouettes are changed into an associated sequence of 1D signals
di=((xi-xc)2+(yi-yc)2)1/2

12. FEATURE EXTRACTION
Model-based approaches
Model-free approaches

13. FEATURE EXTRACTION Model-based approaches
Incorporate structural information of the human body either based on a priori information or through models of the human body deduced from training data.
The benefit of a model-based approach is that a good model allows for robust and consistent feature extraction.

14. Model-based approaches

15. FEATURE EXTRACTION Model-free approaches
Generally aim to extract features based on the movement of the silhouette through time.
The primary advantage of a model-free approach is computational simplicity, as many algorithms of this class can be executed rapidly.

16. Model-free approaches
Gait Energy Image (GEI) algorithm
GEI aims to quantify the gait dynamics of an individual via a single image-based representation.
Given N binary silhouette images, {St(x,y)}, at various time instances denoted by t, the gait energy image is defined as:

17. Model-free approaches
Gait Energy Image (GEI) algorithm

18. CONCLUSION Subject to the effects of different types of clothes.
Other factors:
footwear, walking surface, walking speed, walking direction (with respect to the camera)
Lack of generality of viewing angles.
The gait pattern of an individual can change over time, especially with variations in body mass

19. References
“Silhouette Analysis-Based Gait Recognition for Human Identification”
“Gait Recognition Using Static, Activity-Specific Parameters “
“A Newly Emergent Study: Gait Recognition “