26 research outputs found
Uniscale and multiscale gait recognition in realistic scenario
The performance of a gait recognition method is affected by numerous challenging
factors that degrade its reliability as a behavioural biometrics for subject identification in
realistic scenario. Thus for effective visual surveillance, this thesis presents five gait recog-
nition methods that address various challenging factors to reliably identify a subject in
realistic scenario with low computational complexity. It presents a gait recognition method
that analyses spatio-temporal motion of a subject with statistical and physical parameters
using Procrustes shape analysis and elliptic Fourier descriptors (EFD). It introduces a part-
based EFD analysis to achieve invariance to carrying conditions, and the use of physical
parameters enables it to achieve invariance to across-day gait variation. Although spatio-
temporal deformation of a subjectβs shape in gait sequences provides better discriminative
power than its kinematics, inclusion of dynamical motion characteristics improves the iden-
tification rate. Therefore, the thesis presents a gait recognition method which combines
spatio-temporal shape and dynamic motion characteristics of a subject to achieve robust-
ness against the maximum number of challenging factors compared to related state-of-the-
art methods. A region-based gait recognition method that analyses a subjectβs shape in
image and feature spaces is presented to achieve invariance to clothing variation and carry-
ing conditions. To take into account of arbitrary moving directions of a subject in realistic
scenario, a gait recognition method must be robust against variation in view. Hence, the the-
sis presents a robust view-invariant multiscale gait recognition method. Finally, the thesis
proposes a gait recognition method based on low spatial and low temporal resolution video
sequences captured by a CCTV. The computational complexity of each method is analysed.
Experimental analyses on public datasets demonstrate the efficacy of the proposed methods
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
Inferring Facial and Body Language
Machine analysis of human facial and body language is a challenging topic in computer
vision, impacting on important applications such as human-computer interaction and visual
surveillance. In this thesis, we present research building towards computational frameworks
capable of automatically understanding facial expression and behavioural body language.
The thesis work commences with a thorough examination in issues surrounding facial
representation based on Local Binary Patterns (LBP). Extensive experiments with different
machine learning techniques demonstrate that LBP features are efficient and effective for
person-independent facial expression recognition, even in low-resolution settings. We then
present and evaluate a conditional mutual information based algorithm to efficiently learn the
most discriminative LBP features, and show the best recognition performance is obtained by
using SVM classifiers with the selected LBP features. However, the recognition is performed
on static images without exploiting temporal behaviors of facial expression.
Subsequently we present a method to capture and represent temporal dynamics of facial
expression by discovering the underlying low-dimensional manifold. Locality Preserving Projections
(LPP) is exploited to learn the expression manifold in the LBP based appearance
feature space. By deriving a universal discriminant expression subspace using a supervised
LPP, we can effectively align manifolds of different subjects on a generalised expression manifold.
Different linear subspace methods are comprehensively evaluated in expression subspace
learning. We formulate and evaluate a Bayesian framework for dynamic facial expression
recognition employing the derived manifold representation. However, the manifold representation
only addresses temporal correlations of the whole face image, does not consider
spatial-temporal correlations among different facial regions. We then employ Canonical Correlation Analysis (CCA) to capture correlations among face
parts. To overcome the inherent limitations of classical CCA for image data, we introduce
and formalise a novel Matrix-based CCA (MCCA), which can better measure correlations in
2D image data. We show this technique can provide superior performance in regression and
recognition tasks, whilst requiring significantly fewer canonical factors. All the above work
focuses on facial expressions. However, the face is usually perceived not as an isolated object
but as an integrated part of the whole body, and the visual channel combining facial and
bodily expressions is most informative.
Finally we investigate two understudied problems in body language analysis, gait-based
gender discrimination and affective body gesture recognition. To effectively combine face
and body cues, CCA is adopted to establish the relationship between the two modalities, and
derive a semantic joint feature space for the feature-level fusion. Experiments on large data
sets demonstrate that our multimodal systems achieve the superior performance in gender
discrimination and affective state analysis.Research studentship of Queen Mary, the International Travel Grant of the Royal Academy of Engineering,
and the Royal Society International Joint Project
What else does your biometric data reveal? A survey on soft biometrics
International audienceRecent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics
Vision-based techniques for gait recognition
Global security concerns have raised a proliferation of video surveillance
devices. Intelligent surveillance systems seek to discover possible threats
automatically and raise alerts. Being able to identify the surveyed object can
help determine its threat level. The current generation of devices provide
digital video data to be analysed for time varying features to assist in the
identification process. Commonly, people queue up to access a facility and
approach a video camera in full frontal view. In this environment, a variety of
biometrics are available - for example, gait which includes temporal features
like stride period. Gait can be measured unobtrusively at a distance. The video
data will also include face features, which are short-range biometrics. In this
way, one can combine biometrics naturally using one set of data. In this paper
we survey current techniques of gait recognition and modelling with the
environment in which the research was conducted. We also discuss in detail the
issues arising from deriving gait data, such as perspective and occlusion
effects, together with the associated computer vision challenges of reliable
tracking of human movement. Then, after highlighting these issues and
challenges related to gait processing, we proceed to discuss the frameworks
combining gait with other biometrics. We then provide motivations for a novel
paradigm in biometrics-based human recognition, i.e. the use of the
fronto-normal view of gait as a far-range biometrics combined with biometrics
operating at a near distance