Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm

The ear as a biometric

It is more than 10 years since the first tentative experiments in ear biometrics were conducted and it has now reached the “adolescence” of its development towards a mature biometric. Here we present a timely retrospective of the ensuing research since those early days. Whilst its detailed structure may not be as complex as the iris, we show that the ear has unique security advantages over other biometrics. It is most unusual, even unique, in that it supports not only visual and forensic recognition, but also acoustic recognition at the same time. This, together with its deep three-dimensional structure and its robust resistance to change with age will make it very difficult to counterfeit thus ensuring that the ear will occupy a special place in situations requiring a high degree of protection

Covariate factor mitigation techniques for robust gait recognition

The human gait is a discriminative feature capable of recognising a person by their unique walking manner. Currently gait recognition is based on videos captured in a controlled environment. These videos contain challenges, termed covariate factors, which affect the natural appearance and motion of gait, e.g. carrying a bag, clothing, shoe type and time. However gait recognition has yet to achieve robustness to these covariate factors. To achieve enhanced robustness capabilities, it is essential to address the existing gait recognition limitations. Specifically, this thesis develops an understanding of how covariate factors behave while a person is in motion and the impact covariate factors have on the natural appearance and motion of gait. Enhanced robustness is achieved by producing a combination of novel gait representations and novel covariate factor detection and removal procedures. Having addressed the limitations regarding covariate factors, this thesis achieves the goal of robust gait recognition. Using a skeleton representation of the human figure, the Skeleton Variance Image condenses a skeleton sequence into a single compact 2D gait representation to express the natural gait motion. In addition, a covariate factor detection and removal module is used to maximise the mitigation of covariate factor effects. By establishing the average pixel distribution within training (covariate factor free) representations, a comparison against test (covariate factor) representations achieves effective covariate factor detection. The corresponding difference can effectively remove covariate factors which occur at the boundary of, and hidden within, the human figure.The Engineering and Physical Sciences Research Council (EPSRC

Robust gait recognition under variable covariate conditions

PhDGait is a weak biometric when compared to face, fingerprint or iris because it can be easily affected by various conditions. These are known as the covariate conditions and include clothing, carrying, speed, shoes and view among others. In the presence of variable covariate conditions gait recognition is a hard problem yet to be solved with no working system reported. In this thesis, a novel gait representation, the Gait Flow Image (GFI), is proposed to extract more discriminative information from a gait sequence. GFI extracts the relative motion of body parts in different directions in separate motion descriptors. Compared to the existing model-free gait representations, GFI is more discriminative and robust to changes in covariate conditions. In this thesis, gait recognition approaches are evaluated without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of gait sequences under different and unknown covariate conditions. The results indicate that the performance of the existing approaches drops drastically under this more realistic set-up. It is argued that selecting the gait features which are invariant to changes in covariate conditions is the key to developing a gait recognition system without subject cooperation. To this end, the Gait Entropy Image (GEnI) is proposed to perform automatic feature selection on each pair of gallery and probe gait sequences. Moreover, an Adaptive Component and Discriminant Analysis is formulated which seamlessly integrates the feature selection method with subspace analysis for fast and robust recognition. Among various factors that affect the performance of gait recognition, change in viewpoint poses the biggest problem and is treated separately. A novel approach to address this problem is proposed in this thesis by using Gait Flow Image in a cross view gait recognition framework with the view angle of a probe gait sequence unknown. A Gaussian Process classification technique is formulated to estimate the view angle of each probe gait sequence. To measure the similarity of gait sequences across view angles, the correlation of gait sequences from different views is modelled using Canonical Correlation Analysis and the correlation strength is used as a similarity measure. This differs from existing approaches, which reconstruct gait features in different views through 2D view transformation or 3D calibration. Without explicit reconstruction, the proposed method can cope with feature mis-match across view and is more robust against feature noise