Gait Recognition Gökhan ŞENGÜL

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

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

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

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

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

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

Gait Recognition Algorithm

Human Detection and Tracking

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,

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

FEATURE EXTRACTION Model-based approaches Model-free approaches

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.

Model-based approaches

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.

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:

Model-free approaches Gait Energy Image (GEI) algorithm

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

References “Silhouette Analysis-Based Gait Recognition for Human Identification” http://www.sinobiometrics.com/publications/lwang/PAMI.pdf “Gait Recognition Using Static, Activity-Specific Parameters “ http://www.cc.gatech.edu/cpl/amos/mypapers/cvpr2001_Gait.pdf “A Newly Emergent Study: Gait Recognition “ http://www.sinobiometrics.com/gait.htm