9 research outputs found
Component-based face recognition.
Thesis (M.Sc.)-University of KwaZulu-Natal, 2008.Component-based automatic face recognition has been of interest to a growing number of researchers in the past fifteen years. However, the main challenge remains the automatic extraction of facial components for recognition in different face orientations without any human intervention; or any assumption on the location of these components. In this work, we investigate a solution to this problem. Facial components: eyes, nose, and mouth are firstly detected in different orientations of face. To ensure that the components detected are appropriate for recognition, the Support Vector Machine (SVM) classifer is applied to identify facial components that have been accurately detected. Thereafter, features are extracted from the correctly detected components by Gabor Filters and Zernike Moments combined. Gabor Filters
are used to extract the texture characteristics of the eyes and Zernike Moments are
applied to compute the shape characteristics of the nose and the mouth. The texture
and the shape features are concatenated and normalized to build the final feature vector of the input face image. Experiments show that our feature extraction strategy is robust, it also provides a more compact representation of face images and achieves an average recognition rate of 95% in different face orientations
SYMMETRY IN HUMAN MOTION ANALYSIS: THEORY AND EXPERIMENTS
Video based human motion analysis has been actively studied over the past decades. We propose novel approaches that are able to analyze human motion under such challenges and apply them to surveillance and security applications. Part I analyses the cyclic property of human motion and presents algorithms to classify humans in videos by their gait patterns. Two approaches are proposed. The first employs the omputationally efficient periodogram, to characterize periodicity. In order to integrate shape and motion, we convert the cyclic pattern into a binary sequence using the angle between two legs when the toe-to-toe distance is maximized during walking. Part II further extends the previous approaches to analyze the symmetry in articulation within a stride. A feature that has been shown in our work to be a particularly strong indicator of the presence of pedestrians is the X-junction generated by bipedal swing of body limbs. The proposed algorithm extracts the patterns in spatio-temporal surfaces. In Part III, we present a compact characterization of human gait and activities. Our approach is based on decomposing an image sequence into x-t slices, which generate twisted patterns defined as the Double Helical Signature (DHS). It is shown that the patterns sufficiently characterize human gait and a class of activities. The features of DHS are: (1) it naturally codes appearance and kinematic parameters of human motion; (2) it reveals an inherent geometric symmetry (Frieze Group); and (3) it is effective and efficient for recovering gait and activity parameters. Finally, we use the DHS to classify activities such as carrying a backpack, briefcase etc. The advantage of using DHS is that we only need a small portion of 3D data to recognize various symmetries
IDENTITY CRISIS: WHEN FACE RECOGNITION MEETS TWINS AND PRIVACY
Ph.DDOCTOR OF PHILOSOPH
State of the Art in Face Recognition
Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state
Detection and characterisation of vessels in retinal images.
Doctor of Philosophy in Mathematics, Statistics & Computer Science. University of KwaZulu-Natal, Durban
2015.As retinopathies such as diabetic retinopathy (DR) and retinopathy of
prematurity (ROP) continue to be the major causes of blindness globally,
regular retinal examinations of patients can assist in the early detection of
the retinopathies. The manual detection of retinal vessels is a very tedious
and time consuming task as it requires about two hours to manually detect
vessels in each retinal image. Automatic vessel segmentation has been helpful
in achieving speed, improved diagnosis and progress monitoring of these
diseases but has been challenging due to complexities such as the varying
width of the retinal vessels from very large to very small, low contrast of
thin vessels with respect to background and noise due to nonhomogeneous
illumination in the retinal images. Although several supervised and unsupervised
segmentation methods have been proposed in the literature, the
segmentation of thinner vessels, connectivity loss of the vessels and time
complexity remain the major challenges. In order to address these problems,
this research work investigated di erent unsupervised segmentation
approaches to be used in the robust detection of large and thin retinal vessels
in a timely e cient manner.
Firstly, this thesis conducted a study on the use of di erent global thresholding
techniques combined with di erent pre-processing and post-processing
techniques. Two histogram-based global thresholding techniques namely,
Otsu and Isodata were able to detect large retinal vessels but fail to segment
the thin vessels because these thin vessels have very low contrast and
are di cult to distinguish from the background tissues using the histogram
of the retinal images. Two new multi-scale approaches of computing global
threshold based on inverse di erence moment and sum-entropy combined
with phase congruence are investigated to improve the detection of vessels.
One of the findings of this study is that the multi-scale approaches of computing
global threshold combined with phase congruence based techniques
improved on the detection of large vessels and some of the thin vessels. They,
however, failed to maintain the width of the detected vessels. The reduction
in the width of the detected large and thin vessels results in low sensitivity
rates while relatively good accuracy rates were maintained. Another study
on the use of fuzzy c-means and GLCM sum entropy combined on phase
congruence for vessel segmentation showed that fuzzy c-means combined
with phase congruence achieved a higher average accuracy rates of 0.9431
and 0.9346 but a longer running time of 27.1 seconds when compared with
the multi-scale based sum entropy thresholding combined with phase congruence
with the average accuracy rates of 0.9416 and 0.9318 with a running
time of 10.3 seconds. The longer running time of the fuzzy c-means over the
sum entropy thresholding is, however, attributed to the iterative nature of
fuzzy c-means. When compared with the literature, both methods achieved
considerable faster running time.
This thesis investigated two novel local adaptive thresholding techniques for
the segmentation of large and thin retinal vessels. The two novel local adaptive
thresholding techniques applied two di erent Haralick texture features
namely, local homogeneity and energy. Although these two texture features
have been applied for supervised image segmentation in the literature, their
novelty in this thesis lies in that they are applied using an unsupervised
image segmentation approach. Each of these local adaptive thresholding
techniques locally applies a multi-scale approach on each of the texture
information considering the pixel of interest in relationship with its spacial
neighbourhood to compute the local adaptive threshold. The localised
multi-scale approach of computing the thresholds handled the challenge of
the vessels' width variation. Experiments showed significant improvements
in the average accuracy and average sensitivity rates of these techniques
when compared with the previously discussed global thresholding methods
and state of the art. The two novel local adaptive thresholding techniques
achieved a higher reduction of false vessels around the border of the optic
disc when compared with some of the previous techniques in the literature.
These techniques also achieved a highly improved computational time of 1.9
to 3.9 seconds to segment the vessels in each retinal image when compared
with the state of the art. Hence, these two novel local adaptive thresholding
techniques are proposed for the segmentation of the vessels in the retinal
images.
This thesis further investigated the combination of di erence image and kmeans
clustering technique for the segmentation of large and thin vessels in
retinal images. The pre-processing phase computed a di erence image and
k-means clustering technique was used for the vessel detection. While investigating
this vessel segmentation method, this thesis established the need
for a difference image that preserves the vessel details of the retinal image.
Investigating the di erent low pass filters, median filter yielded the best
di erence image required by k-means clustering for the segmentation of the
retinal vessels. Experiments showed that the median filter based di erence
images combined with k-means clustering technique achieved higher average
accuracy and average sensitivity rates when compared with the previously
discussed global thresholding methods and the state of the art. The median
filter based di erence images combined with k-means clustering technique
(that is, DIMDF) also achieved a higher reduction of false vessels around
the border of the optic disc when compared with some previous techniques
in the literature. These methods also achieved a highly improved computational
time of 3.4 to 4 seconds when compared with the literature. Hence,
the median filter based di erence images combined with k-means clustering
technique are proposed for the segmentation of the vessels in retinal images.
The characterisation of the detected vessels using tortuosity measure was
also investigated in this research. Although several vessel tortuosity methods
have been discussed in the literature, there is still need for an improved
method that e ciently detects vessel tortuosity. The experimental study
conducted in this research showed that the detection of the stationary points
helps in detecting the change of direction and twists in the vessels. The
combination of the vessel twist frequency obtained using the stationary
points and distance metric for the computation of normalised and nonnormalised
tortuosity index (TI) measure was investigated. Experimental
results showed that the non-normalised TI measure had a stronger correlation
with the expert's ground truth when compared with the distance
metric and normalised TI measures. Hence, a non-normalised TI measure
that combines the vessel twist frequency based on the stationary points and
distance metric is proposed for the measurement of vessel tortuosity