UWCScholar :: Browsing by Author "Connan, James"

Browsing by Author "Connan, James"

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Autonomous facial expression recognition using the facial action coding system
(University of the Western Cape, 2016) de la Cruz, Nathan; Ghaziasgar, Mehrdad; Connan, James
The South African Sign Language research group at the University of the Western Cape is in the process of creating a fully-edged machine translation system to automatically translate between South African Sign Language and English. A major component of the system is the ability to accurately recognise facial expressions, which are used to convey emphasis, tone and mood within South African Sign Language sentences. Traditionally, facial expression recognition research has taken one of two paths: either recognising whole facial expressions of which there are six i.e. anger, disgust, fear, happiness, sadness, surprise, as well as the neutral expression; or recognising the fundamental components of facial expressions as defined by the Facial Action Coding System in the form of Action Units. Action Units are directly related to the motion of specific muscles in the face, combinations of which are used to form any facial expression. This research investigates enhanced recognition of whole facial expressions by means of a hybrid approach that combines traditional whole facial expression recognition with Action Unit recognition to achieve an enhanced classification approach.
Avatar animation from SignWriting notation
(University of the Western Cape, 2015) Abrahams, Kenzo; Ghaziasgar, Mehrdad; Connan, James
The SASL project at the University of the Western Cape is in the process of developing a machine translation system that can translate fully-fledged phrases between South African Sign Language (SASL) and English in real-time.To visualise sign language,the system aims to make use of a 3D humanoid avatar created by van Wyk. Moemedi used this avatar to create an animation system that visualises a small set of simple Phrases from very simple SignWriting notation input. This research aims to achieve an animation system that can render full sign language sentences given complex SignWriting notation glyphs with multiple sections. The specific focus of the research is achieving animations that are accurate representations of the SignWriting input in terms of the five fundamental parameters of sign language, namely, hand motion, location, orientation and shape, as well as non-manual features such as facial expressions. An experiment Was carried out to determine the accuracy of the proposed system on a set of 20 SASL phrases annotated with SignWriting notation. It was found that the proposed system is highly accurate, achieving an average accuracy of 81.6%.
A comparison of machine learning techniques for hand shape recognition
(University of the Western Cape, 2015) Foster, Roland; Ghaziasgar, Mehrdad; Connan, James
There are five fundamental parameters that characterize any sign language gesture. They are hand shape, orientation, motion and location, and facial expressions. The SASL group at the University of the Western Cape has created systems to recognize each of these parameters in an input video stream. Most of these systems make use of the Support Vector Machine technique for the classification of data due to its high accuracy. It is, however, unknown how other machine learning techniques compare to Support Vector Machines in the recognition of each of these parameters. This research lays the foundation for the process of determining optimum machine learning techniques for each parameter by comparing Support Vector Machines to Artificial Neural Networks and Random Forests in the context of South African Sign Language hand shape recognition. Li, a previous researcher at the SASL group, created a state-of-the-art hand shape recognition system that uses Support Vector Machines to classify hand shapes. This research re-implements Li’s feature extraction procedure but investigates the use of Artificial Neural Networks and Random Forests in the place of Support Vector Machines as a comparison. The machine learning techniques are optimized and trained to recognize ten SASL hand shapes and compared in terms of classification accuracy, training time, optimization time and classification time.
The Efficacy of the Eigenvector Approach to South African Sign Language Identification
(University of the Western Cape, 2010) Segers, Vaughn Mackman; Connan, James
The communication barriers between deaf and hearing society mean that interaction between these communities is kept to a minimum. The South African Sign Language research group, Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL), at the University of the Western Cape aims to create technologies to bridge the communication gap. In this thesis we address the subject of whole hand gesture recognition. We demonstrate a method to identify South African Sign Language classifiers using an eigenvector approach. The classifiers researched within this thesis are based on those outlined by the Thibologa Sign Language Institute for SASL. Gesture recognition is achieved in real time. Utilising a pre-processing method for image registration we are able to increase the recognition rates for the eigenvector approach.
The efficacy of the Eigenvector approach to South African sign language identification
(University of the Western Cape, 2010) Segers, Vaughn Mackman; Connan, James; Dept. of Computer Science; Faculty of Science
The communication barriers between deaf and hearing society mean that interaction between these communities is kept to a minimum. The South African Sign Language research group, Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL), at the University of the Western Cape aims to create technologies to bridge the communication gap. In this thesis we address the subject of whole hand gesture recognition. We demonstrate a method to identify South African Sign Language classifiers using an eigenvector ap- proach. The classifiers researched within this thesis are based on those outlined by the Thibologa Sign Language Institute for SASL. Gesture recognition is achieved in real- time. Utilising a pre-processing method for image registration we are able to increase the recognition rates for the eigenvector approach.
Hand shape estimation for South African sign language
(University of the Western Cape, 2012) Li, Pei; Connan, James; Ghaziasgar, Mehrdad
Hand shape recognition is a pivotal part of any system that attempts to implement Sign Language recognition. This thesis presents a novel system which recognises hand shapes from a single camera view in 2D. By mapping the recognised hand shape from 2D to 3D,it is possible to obtain 3D co-ordinates for each of the joints within the hand using the kinematics embedded in a 3D hand avatar and smooth the transformation in 3D space between any given hand shapes. The novelty in this system is that it does not require a hand pose to be recognised at every frame, but rather that hand shapes be detected at a given step size. This architecture allows for a more efficient system with better accuracy than other related systems. Moreover, a real-time hand tracking strategy was developed that works efficiently for any skin tone and a complex background.
An integrated sign language recognition system
(University of Western Cape, 2014) Nel, Warren; Ghaziasgar, Mehrdad; Connan, James
Research has shown that five parameters are required to recognize any sign language gesture: hand shape, location, orientation and motion, as well as facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape has created systems to recognize Sign Language gestures using single parameters. Using a single parameter can cause ambiguities in the recognition of signs that are similarly signed resulting in a restriction of the possible vocabulary size. This research pioneers work at the group towards combining multiple parameters to achieve a larger recognition vocabulary set. The proposed methodology combines hand location and hand shape recognition into one combined recognition system. The system is shown to be able to recognize a very large vocabulary of 50 signs at a high average accuracy of 74.1%. This vocabulary size is much larger than existing SASL recognition systems, and achieves a higher accuracy than these systems in spite of the large vocabulary. It is also shown that the system is highly robust to variations in test subjects such as skin colour, gender and body dimension. Furthermore, the group pioneers research towards continuously recognizing signs from a video stream, whereas existing systems recognized a single sign at a time. To this end, a highly accurate continuous gesture segmentation strategy is proposed and shown to be able to accurately recognize sentences consisting of five isolated SASL gestures.
KernTune: Self-tuning Linux kernel performance using support vector machines
(Association for Computing Machinery, 2007) Yi, Long; Connan, James
Self-tuning has been an elusive goal for operating systems and is becoming a pressing issue for modern operating systems. Well-trained system administrators are able to tune an operating system to achieve better system performance for a specific system class. Unfortunately, the system class can change when the running applications change. Our model for self-tuning operating system is based on a monitor-classify-adjust loop. The idea of this loop is to continuously monitor certain performance metrics, and whenever these change, the system determines the new system class and dynamically adjusts tuning parameters for this new class. This paper describes KernTune, a prototype tool that identifies the system class and improves system performance automatically. A key aspect of KernTune is the notion of Artificial Intelligence (AI) oriented performance tuning. It uses a support vector machine (SVM) to identify the system class, and tunes the operating system for that specific system class. This paper presents design and implementation details for KernTune. It shows how KernTune identifies a system class and tunes the operating system for improved performance.
KernTune: Self-tuning Linux Kernel Performance Using Support Vector Machines
(University of the Western Cape, 2006) Yi, Long; Connan, James
Self-tuning has been an elusive goal for operating systems and is becoming a pressing issue for modern operating systems. Well-trained system administrators are able to tune an operating system to achieve better system performance for a specific system class. Unfortunately, the system class can change when the running applications change. Our model for self-tuning operating system is based on a monitor-classify- adjust loop. The idea of this loop is to continuously monitor certain performance metrics, and whenever these change, the system determines the new system class and dynamically adjusts tuning parameters for this new class. This thesis describes KernTune, a prototype tool that identifies the system class and improves system performance automatically. A key aspect of KernTune is the notion of Artificial Intelligence (AI) oriented performance tuning. It uses a support vector machine (SVM) to identify the system class, and tunes the operating system for that specific system class. This thesis presents design and implementation details for KernTune. It shows how KernTune identifies a system class and tunes the operating system for improved performance.
KernTune: self-tuning Linux kernel performance using support vector machines
(University of the Western Cape, 2006) Yi, Long; Connan, James; Dept. of Computer Science; Faculty of Science
Self-tuning has been an elusive goal for operating systems and is becoming a pressing issue for modern operating systems. Well-trained system administrators are able to tune an operating system to achieve better system performance for a specific system class. Unfortunately, the system class can change when the running applications change. The model for self-tuning operating system is based on a monitor-classify-adjust loop. The idea of this loop is to continuously monitor certain performance metrics, and whenever these change, the system determines the new system class and dynamically adjusts tuning parameters for this new class. This thesis described KernTune, a prototype tool that identifies the system class and improves system performance automatically. A key aspect of KernTune is the notion of Artificial Intelligence oriented performance tuning. Its uses a support vector machine to identify the system class, and tunes the operating system for that specific system class. This thesis presented design and implementation details for KernTune. It showed how KernTune identifies a system class and tunes the operating system for improved performance.
Real-time gesture recognition using eigenvectors
(2009) Segers, Vaughn; Connan, James
This paper discusses an implementation for gesture recognition using eigenvectors under controlled conditions. This application of eigenvector recognition is trained on a set of defined hand images. Training images are processed using eigen techniques from the OpenCV image processing library. Test images are then compared in real-time. These techniques are outlined below.
Rendering an avatar from sign writing notation for sign language animation
(University of the Western Cape, 2010) Moemedi, Kgatlhego Aretha; Connan, James; Dept. of Computer Science; Faculty of Science
This thesis presents an approach for automatically generating signing animations from a sign language notation. An avatar endowed with expressive gestures, as subtle as changes in facial expression, is used to render the sign language animations. SWML, an XML format of SignWriting is provided as input. It transcribes sign language gestures in a format compatible to virtual signing. Relevant features of sign language gestures are extracted from the SWML. These features are then converted to body animation pa- rameters, which are used to animate the avatar. Using key-frame animation techniques, intermediate key-frames approximate the expected sign language gestures. The avatar then renders the corresponding sign language gestures. These gestures are realistic and aesthetically acceptable and can be recognized and understood by Deaf people.
A robust audio-based symbol recognition system using machine learning techniques
(University of the Western Cape, 2020-02) Wu, Qiming; Ghaziasgar, Mehrdad; Connan, James; Dodds, Reg
This research investigates the creation of an audio-shape recognition system that is able to interpret a user’s drawn audio shapes—fundamental shapes, digits and/or letters— on a given surface such as a table-top using a generic stylus such as the back of a pen. The system aims to make use of one, two or three Piezo microphones, as required, to capture the sound of the audio gestures, and a combination of the Mel-Frequency Cepstral Coeﬃcients (MFCC) feature descriptor and Support Vector Machines (SVMs) to recognise audio shapes. The novelty of the system is in the use of piezo microphones which are low cost, light-weight and portable, and the main investigation is around determining whether these microphones are able to provide suﬃciently rich information to recognise the audio shapes mentioned in such a framework.
Robust facial expression recognition in the presence of rotation and partial occlusion
(University of Western Cape, 2014) Mushfieldt, Diego; Ghaziasgar, Mehrdad; Connan, James
This research proposes an approach to recognizing facial expressions in the presence of rotations and partial occlusions of the face. The research is in the context of automatic machine translation of South African Sign Language (SASL) to English. The proposed method is able to accurately recognize frontal facial images at an average accuracy of 75%. It also achieves a high recognition accuracy of 70% for faces rotated to 60◦. It was also shown that the method is able to continue to recognize facial expressions even in the presence of full occlusions of the eyes, mouth and left/right sides of the face. The accuracy was as high as 70% for occlusion of some areas. An additional finding was that both the left and the right sides of the face are required for recognition. As an addition, the foundation was laid for a fully automatic facial expression recognition system that can accurately segment frontal or rotated faces in a video sequence.
Robust recognition of facial expressions on noise degraded facial images
(University of the Western Cape, 2011) Sheikh, Munaf; Connan, James; Dept. of Computer Science; Faculty of Science
We investigate the use of noise degraded facial images in the application of facial expression recognition. In particular, we trained Gabor+SVMclassifiers to recognize facial expressions images with various types of noise. We applied Gaussian noise, Poisson noise, varying levels of salt and pepper noise, and speckle noise to noiseless facial images. Classifiers were trained with images without noise and then tested on the images with noise. Next, the classifiers were trained using images with noise, and then on tested both images that had noise, and images that were noiseless. Finally, classifiers were tested on images while increasing the levels of salt and pepper in the test set. Our results reflected distinct degradation of recognition accuracy. We also discovered that certain types of noise, particularly Gaussian and Poisson noise, boost recognition rates to levels greater than would be achieved by normal, noiseless images. We attribute this effect to the Gaussian envelope component of Gabor filters being sympathetic to Gaussian-like noise, which is similar in variance to that of the Gabor filters. Finally, using linear regression, we mapped a mathematical model to this degradation and used it to suggest how recognition rates would degrade further should more noise be added to the images.
Robust South African sign language gesture recognition using hand motion and shape
(2014) Frieslaar, Ibraheem; Ghaziasgar, Mehrdad; Connan, James
Research has shown that five fundamental parameters are required to recognize any sign language gesture: hand shape, hand motion, hand location, hand orientation and facial expressions. The South African Sign Language (SASL) research group at the University of the Western Cape (UWC) has created several systems to recognize sign language gestures using single parameters. These systems are, however, limited to a vocabulary size of 20 – 23 signs, beyond which the recognition accuracy is expected to decrease. The first aim of this research is to investigate the use of two parameters – hand motion and hand shape – to recognise a larger vocabulary of SASL gestures at a high accuracy. Also, the majority of related work in the field of sign language gesture recognition using these two parameters makes use of Hidden Markov Models (HMMs) to classify gestures. Hidden Markov Support Vector Machines (HM-SVMs) are a relatively new technique that make use of Support Vector Machines (SVMs) to simulate the functions of HMMs. Research indicates that HM-SVMs may perform better than HMMs in some applications. To our knowledge, they have not been applied to the field of sign language gesture recognition. This research compares the use of these two techniques in the context of SASL gesture recognition. The results indicate that, using two parameters results in a 15% increase in accuracy over the use of a single parameter. Also, it is shown that HM-SVMs are a more accurate technique than HMMs, generally performing better or at least as good as HMMs.
Self-tuning Linux Kernel Performance Using Support Vector Machines
(University of the Western Cape, 2006) Yi, Long; Connan, James
In this chapter, we provide the motivation and background behind the automatic optimisation of an operating system. We begin with a discussion of some of the difficulties of automatic operating system optimisation and the benefits of automatic optimisation technology which inspired our research. We then describe the research problem and aims. Thereafter, our approach and methodology are explained. Finally, the organisation of the thesis and summary are presented. 1.1 Background and Motivation In today's networking world, a mission-critical server requires consistently good performance [2] . To this end, almost all operating systems which run on such a critical server are managed by system administrators who should be skillful and experienced in tuning operating systems by adjusting system configuration and performance parameters of the operating system to run a specific system workload. This involves system capacity planning, performance metrics, workload characteristics, system settings, etc. Skillful system administrators are scarce and expensive. As computer hardware becomes cheaper and free critical computer software becomes more viable, e.g., Linux, Samba, Mysql, Apache, the total cost of ownership for building and maintaining a mission-critical server becomes more and more dominated by the cost of human resources. Furthermore, with the increasing number of new applications and services, a modern operating system offers more system parameters with larger ranges for more system classes than ever before. This situation serves as our motivation for a new generation of automatic optimisation technology for operating systems. The potential benefits of the automatic optimisation technology will be amplified as future applications and operating systems become more complex.
South African sign language recognition using feature vectors and Hidden Markov Models
(University of the Western Cape, 2010) Naidoo, Nathan Lyle; Connan, James; Dept. of Computer Science; Faculty of Science
This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to constuct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.
South African Sign Language Recognition Using Feature Vectors and Hidden Markov Models
(University of the Western Cape, 2010) Naidoo, Nathan Lyle; Connan, James
This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to construct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.
Static MySQL Error Checking
(University of the Western Cape, 2010) Zarinkhail, Mohammad Shuaib; Connan, James
Coders of databases repeatedly face the problem of checking their Structured Query Language (SQL) code. Instructors face the difficulty of checking student projects and lab assignments in database courses. We collect and categorize common MySQL programming errors into three groups: data definition errors, data manipulation errors, and transaction control errors. We build these into a comprehensive list of MySQL errors, which novices are inclined make during database programming. We collected our list of common MySQL errors both from the technical literature and directly by noting errors made in assignments handed in by students. In the results section of this research, we check and summarize occurrences of these errors based on three characteristics as semantics, syntax, and logic. These data form the basis of a future static MySQL checker that will eventually assist database coders to correct their code automatically. These errors also form a useful checklist to guide students away from the mistakes that they are prone to make.

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