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

Investigation of gait representations and partial body gait recognition

Recognising an individual by the way they walk is one of the most popular research subjects within the field of soft biometrics in last few decades. The advancement of technology and equipment such as Close Circuit Television (CCTV), wireless internet and wearable sensors makes it easier to obtain gait data than ever before. The gait biometric can be used widely and in different areas such as biomedical, forensic and surveillance. However, gait recognition still has many challenges and fundamental issues. All of these problems only serve as a researcher’s motivation to learn more about various gait topics to overcome the challenges and improve the field of gait recognition. Gait recognition currently has high performance when carried out under very specific conditions such as normal walking, obstruction from certain types of clothing and fixed camera view angles. When the aforementioned conditions are changed, the classification rate dramatically drops. This study aims to solve the problems of clothing, carrying objects and camera view angles within the indoor environment and video-based data collection. Two gait related databases used for testing in this study are CASIA dataset B and OU-ISIR Large population dataset with Bag (OU-LP-Bag). Three main tasks will be tested with CASIA dataset B while only gait recognition is tested with OU-LP-Bag. The gait recognition framework is developed to solve the three main tasks including gait recognition by identical view, view classification and cross view recognition. This framework uses gait images sequence as input to generate a gait compact image. Next, gait features are extracted with the optimal feature map by Principal Component Analysis (PCA) and then a linear Support Vector Machine (SVM) is used as the one-against-all multiclass classifier. Four gait compact images including Gait Energy Image (GEI), Gait Entropy Image (GEnI), Gait Gaussian Image (GGI) and the novel gait images called Gait Gaussian Entropy Image (GGEnI) are used as basic gait representations. Then three secondary gait representations are generated from these basic representations. These include Gradient Histogram Gait Image (GHGI) and two novel gait representations called Convolutional Gait Image (CGI) and Convolutional Gradient Histogram Gait Image (CGHGI). All representations are tested with three main tasks. When people walk, each body part does not have the same locomotion information, for example, there is much more motion in the leg than shoulder motion when walking. Moreover, clothing and carrying objects do not have the same level of affect to every part of the body, for example, a handbag does not generally affect leg motion. This study divides the human body into fourteen different body parts based on height. Body parts and gait representations are combined to solve the three main tasks. Three combined parts techniques which use two different parts to solve the problem are created. The fist is Part Scores Fusion (PSF) which uses the summation score of two models based on each part. The highest summation score model is chosen as the result. The second is Part Image Fusion (PIF) which concatenates two parts into a single image with a 1:1 ratio. The highest scoring model which is generated from image fusion is selected as the result. The third is Multi Region Duplication (MRD) which uses the same idea as PIF, however, the second part’s ratio is increased to 1:2, 1:3 and 1:4. These techniques are tested on the gait recognition by identical view. In conclusion, the general framework is effectively for three main tasks. GHGI-GEI which is generated from full silhouette is the most effective representation for gait recognition by identical view and cross view recognition. GHGI-GGI with lower knee region is the most effective representation for view angle classification. The GHGI-GEI CPI combination between full body and limb parts is the most effective combination on OU-LP-Bag. A more detailed description of each aspect is in the following Chapters

MMUGait database and baseline results

This paper describes the acquisition setup and development of a new gait database, MMUGait DB. The database was captured in side and oblique views, where 82 subjects participated under normal walking conditions and 19 subjects walking under 11 covariate factors. The database includes ‘sarong’ and ‘kain samping’ as changes of apparel, which are the traditional costumes for ethnic Malays in South East Asia. Classification experiments were carried out on MMUGait DB and the baseline results are presented for validation purposes