Gait Recognition Simon Smith Jamie Hutton Thomas Moore David Newman

Outline Gait? Gait? Enrolment Enrolment Symmetry Symmetry Hu Invariant Moments Hu Invariant Moments Classification Classification Conclusions Conclusions Demonstration Demonstration

Gait? From Old Norse gata for “path” From Old Norse gata for “path” –But possibly from Northern derivative of goat Why use gait as a biometric? Why use gait as a biometric? –Non-invasive –Process sequence of images  More information than other biometrics  Greater robustness/reliability Gait recognition methods Gait recognition methods –Model-based –Holistic approach

Holistic Methods Chose to implement two holistic approaches Chose to implement two holistic approaches –Less computationally complex, faster –More suitable for online demonstration A simple representation of Gait A simple representation of Gait –Raw numbers, images –Problems with occlusion, noise

Enrolment Capture subject’s gait Capture subject’s gait –Video ideally with chroma-key background –Avoid occlusion of subject –Outdoor images cause some problems Process video of subject walking 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

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

Hu Invariant Moments Shape descriptor, combines moments to give invariance to Shape descriptor, combines moments to give invariance to –Rotation –Translation –Scaling Originally designed for single shape description, extended here for sequences Originally designed for single shape description, extended here for sequences –We use Hu1, Hu2 and Hu8 moments –Other moments fail to discriminate between subjects

Classification k Nearest Neighbours classification k Nearest Neighbours classification –Euclidean distance –Up to 6-dimensional feature space  Mean of 1 or all Hu moments  Variance of 1 or all Hu moments –Tie Resolution  Highest ranking matches chosen

Conclusions Evaluation of two Holistic Gait descriptors Evaluation of two Holistic Gait descriptors –Hu Moments  Good indoor performance  Poor performance outdoor  Needs higher dimensional parameter space  Ability to ignore/correct anomalous results –Symmetry  Good indoor, better outdoor performance  Larger population may cause poor performance

Demonstration