251 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 subjectโs 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 subjectโs 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
Gait Analysis for Gender Classification in Forensics
Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with arbitrary poses, facial expressions, occlusions and motion blur. In this work we present a methodology for the construction of a gait analyzer. The methodology is divided into three major steps: (1) data extraction, where body keypoints are extracted from video sequences; (2) feature creation, where body features are constructed using body keypoints; and (3) classifier selection when such data are used to train four different classifiers in order to determine the one that best performs. The results are analyzed on the dataset Gotcha, characterized by user and camera either in motion
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
Green strength optimization of injection molding proces for novel recycle binder system using Taguchi method
Metal injection molding is a worldwide technology that world use as a predominant method in manufacturing. Optimizing the injection molding process is critical in obtaining a good shape retention of green components and improving manufacturing processes itself. This research focuses on the injection molding optimization which correlated to a single response of green strength which implementing orthogonal array of Taguchi L9 (34). It involved the effect of four molding factors: injection temperature, mold temperature, injection pressure and injection speed, towards green strength. The significant levels and contribution to the variables of green strength are determined using the analysis of variance. Manual screening test is conducted in regards of identifying the appropriate level of each factors. The study demonstrated that injection temperature was the most influential factor contributes to the best green strength, followed by mold temperature, injection speed and injection pressure. The optimum condition for attaining optimal green strength was definitely by conducting injection molding at; 160 ยบC of injection temperature, 40 ยบC of mold temperature, 50 % of injection pressure and 50 % of injection speed. The confirmation experiment result is 15.5127 dB and it was exceeding minimum requirement of the optimum performance. This research reveals that the proposed approach can excellently solve the problem with minimal number of trials, without sacrificing the ability of evaluating the appropriate condition to achieve related response, which is green strength
- โฆ