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An Investigation into the Relationship between Static and Dynamic Gait Features. A biometrics Perspective
Biometrics is a unique physical or behavioral characteristic of a person. This unique attribute, such as fingerprints or gait, can be used for identification or verification purposes. Gait is an emerging biometrics with great potential. Gait recognition is based on recognizing a person by the manner in which they walk. Its potential lays in that it can be captured at a distance and does not require the cooperation of the subject. This advantage makes it a very attractive tool for forensic cases and applications, where it can assist in identifying a suspect when other evidence such as DNA, fingerprints, or a face were not attainable. Gait can be used for recognition in a direct manner when the two samples are shot from similar camera resolution, position, and conditions. Yet in some cases, the only sample available is of an incomplete gait cycle, low resolution, low frame rate, a partially visible subject, or a single static image. Most of these conditions have one thing in common: static measurements. A gait signature is usually formed from a number of dynamic and static features. Static features are physical measurements of height, length, or build; while dynamic features are representations of joint rotations or trajectories.
The aim of this thesis is to study the potential of predicting dynamic features from static features. In this thesis, we have created a database that utilizes a 3D laser scanner for capturing accurate shape and volumes of a person, and a motion capture system to accurately record motion data. The first analysis focused on analyzing the correlation between twenty-one 2D static features and eight dynamic features. Eleven pairs of features were regarded as significant with the criterion of a P-value less than 0.05. Other features also showed a strong correlation that indicated the potential of their predictive power. The second analysis focused on 3D static and dynamic features. Through the correlation analysis, 1196 pairs of features were found to be significantly correlated. Based on these results, a linear regression analysis was used to predict a dynamic gait signature. The predictors chosen were based on two adaptive methods that were developed in this thesis: "the top-x" method and the "mixed method". The predictions were assessed for both for their accuracy and their classification potential that would be used for gait recognition. The top results produced a 59.21% mean matching percentile. This result will act as baseline for future research in predicting a dynamic gait signature from static features. The results of this thesis bare potential for applications in biomechanics, biometrics, forensics, and 3D animation
Bradford Multi-Modal Gait Database: Gateway to Using Static Measurements to Create a Dynamic Gait Signature
YesAims: To create a gait database with optimum accuracy of joint rotational data and an accu-rate
representation of 3D volume, and explore the potential of using the database in studying the
relationship between static and dynamic features of a human’s gait.
Study Design: The study collected gait samples from 38 subjects, in which they were asked to
walk, run, walk to run transition, and walk with a bag. The motion capture, video, and 3d
measurement data extracted was used to analyse and build a correlation between features.
Place and Duration of Study: The study was conducted in the University of Bradford. With the
ethical approval from the University, 38 subjects’ motion and body volumes were recorded at the
motion capture studio from May 2011- February 2013.
Methodology: To date, the database includes 38 subjects (5 females, 33 males) conducting walk
cycles with speed and load as covariants. A correlation analysis was conducted to ex-plore the
potential of using the database to study the relationship between static and dynamic features. The
volumes and surface area of body segments were used as static features. Phased-weighted
magnitudes extracted through a Fourier transform of the rotation temporal data of the joints from the motion capture were used as dynamic features. The Pearson correlation coefficient is used to
evaluate the relationship between the two sets of data.
Results: A new database was created with 38 subjects conducting four forms of gait (walk, run,
walk to run, and walking with a hand bag). Each subject recording included a total of 8 samples of
each form of gait, and a 3D point cloud (representing the 3D volume of the subject). Using a Pvalue
(P<.05) as a criterion for statistical significance, 386 pairs of features displayed a strong
relationship.
Conclusion: A novel database available to the scientific community has been created. The
database can be used as an ideal benchmark to apply gait recognition techniques, and based on
the correlation analysis, can offer a detailed perspective of the dynamics of gait and its relationship
to volume. Further research in the relationship between static and dynamic features can contribute
to the field of biomechanical analysis, use of biometrics in forensic applications, and 3D virtual walk
simulation