UWCScholar :: Browsing by Author "Ghaziasgar, Mehrdad"

Browsing by Author "Ghaziasgar, Mehrdad"

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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%.
Basic statistical estimation outperforms machine learning in monthly prediction of seasonal climatic parameters
(MPDI, 2021) Hussein, Eslam A.; Ghaziasgar, Mehrdad; Thron, Christopher
Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.
A comparative evaluation of 3d and spatio-temporal deep learning techniques for crime classification and prediction
(University of Western Cape, 2021) Matereke, Tawanda Lloyd; Ghaziasgar, Mehrdad
This research is on a comparative evaluation of 3D and spatio-temporal deep learning methods for crime classification and prediction using the Chicago crime dataset, which has 7.29 million records, collected from 2001 to 2020. In this study, crime classification experiments are carried out using two 3D deep learning algorithms, i.e., 3D Convolutional Neural Network and the 3D Residual Network. The crime classification models are evaluated using accuracy, F1 score, Area Under Receiver Operator Curve (AUROC), and Area Under Curve - Precision-Recall (AUCPR). The effectiveness of spatial grid resolutions on the performance of the classification models is also evaluated during training, validation and testing.
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.
Comparison of phenolic content and antioxidant activity for fermented and unfermented rooibos samples extracted with water and methanol
(MPDI, 2022) Hussein, Eslam A.; Thron, Christopher; Ghaziasgar, Mehrdad
Rooibos is brewed from the medicinal plant Aspalathus linearis. It has a well-established wide spectrum of bio-activity properties, which in part may be attributed to the phenolic antioxidant power. The antioxidant capacity (AOC) of rooibos is related to its total phenolic content (TPC). The relation between TPC and AOC of randomly selected 51 fermented (FR) and 47 unfermented (UFR) rooibos samples was studied after extraction using water and methanol separately. The resulted extracts were assessed using two antioxidant assays, trolox equivalent antioxidant capacity (TEAC) and ferric reducing antioxidant power (FRAP). The results were analyzed using both simple statistical methods and machine learning. The analysis showed different trends of TPC and AOC correlations of FR and UFR samples, depending on the solvent used for extraction. The results of the water extracts showed similar TPC and higher AOC of FR than UFR samples, while the methanolic extracted samples showed higher TPC and AOC of UFR than FR. As a result, the methanolic extracts showed better agreement between TPC and AOC than water extracts.
Complex sequential data analysis: A systematic literature review of existing algorithms
(SAICSIT, 2020) Dandajena, Kudakwashe; Venter, Isabella M.; Ghaziasgar, Mehrdad
This paper provides a review of past approaches to the use of deep-learning frameworks for the analysis of discrete irregularpatterned complex sequential datasets. A typical example of such a dataset is financial data where specific events trigger sudden irregular changes in the sequence of the data. Traditional deep-learning methods perform poorly or even fail when trying to analyse these datasets. The results of a systematic literature review reveal the dominance of frameworks based on recurrent neural networks.
Faster upper body pose recognition and estimation using compute unified device architecture
(University of Western Cape, 2013) Brown, Dane; Ghaziasgar, Mehrdad; James Connan, James
The SASL project is in the process of developing a machine translation system that can translate fully-fledged phrases between SASL and English in real-time. To-date, several systems have been developed by the project focusing on facial expression, hand shape, hand motion, hand orientation and hand location recognition and estimation. Achmed developed a highly accurate upper body pose recognition and estimation system. The system is capable of recognizing and estimating the location of the arms from a twodimensional video captured from a monocular view at an accuracy of 88%. The system operates at well below real-time speeds. This research aims to investigate the use of optimizations and parallel processing techniques using the CUDA framework on Achmed’s algorithm to achieve real-time upper body pose recognition and estimation. A detailed analysis of Achmed’s algorithm identified potential improvements to the algorithm. Are- implementation of Achmed’s algorithm on the CUDA framework, coupled with these improvements culminated in an enhanced upper body pose recognition and estimation system that operates in real-time with an increased accuracy.
Geo-physical parameter forecasting on imagery{based data sets using machine learning techniques
(University of Western Cape, 2021) Hussein, Eslam; Ghaziasgar, Mehrdad
This research objectively investigates the e ectiveness of machine learning (ML) tools towards predicting several geo-physical parameters. This is based on a large number of studies that have reported high levels of prediction success using ML in the eld. Therefore, several widely used ML tools coupled with a number of di erent feature sets are used to predict six geophysical parameters namely rainfall, groundwater, evapora- tion, humidity, temperature, and wind. The results of the research indicate that: a) a large number of related studies in the eld are prone to speci c pitfalls that lead to over-estimated results in favour of ML tools; b) the use of gaussian mixture models as global features can provide a higher accuracy compared to other local feature sets; c) ML never outperform simple statistically-based estimators on highly-seasonal parame- ters, and providing error bars is key to objectively evaluating the relative performance of the ML tools used; and d) ML tools can be e ective for parameters that are slow- changing such as groundwater.
Groundwater prediction using machine-learning tools
(MPDI, 2020) Hussein, Eslam A.; Thron, Christopher; Ghaziasgar, Mehrdad
Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water. This paper proposes a machine-learning approach to groundwater prediction with the following characteristics: (i) the use of a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps; (ii) strategic automatic feature selection (both local and global features) using extreme gradient boosting; and (iii) the use of a multiplicity of machine-learning techniques (extreme gradient boosting, multivariate linear regression, random forests, multilayer perceptron and support vector regression). Of these techniques, support vector regression consistently performed best in terms of minimizing root mean square error and mean absolute error. Furthermore, including a global feature obtained from a Gaussian Mixture Model produced models with lower error than the best which could be obtained with local geographical features.
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.
Handwritten alphabet character recognition using audio signatures and machine learning
(University of the Western Cape, 2023) Beck, Bruce; Ghaziasgar, Mehrdad
This research investigates the creation of an audio-based character recognition system that is able to segment, process and recognise uppercase English letters continuously drawn by the user on a given writing surface such as a table-top using a generic writing implement. The aim is to make use of the microphones on a single smartphone to capture the acoustic signal generated by the user as they draw letters on the writing surface, followed by the application of audio segmentation to subdivide the audio signal into segments corresponding to each letter, and finally the application of a combination of the Mel-Frequency Cepstral Coefficients feature descriptor and Support Vector Machines to recognise the segmented letters.
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.
Regional rainfall prediction using support vector machine classification of large-scale precipitation maps
(Institute of Electrical and Electronics Engineers Inc., 2020) Hussein, Eslam A.; Ghaziasgar, Mehrdad; Thron, Christopher
Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1-30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continental US, with rainfall quantized at 3 levels: light or no rain; moderate; and heavy rain. Three regions were selected, corresponding to three squares from a 5 × 5 grid covering the map area. Rainfall predictions up to 30 days ahead for these three regions were based on a support vector machine (SVM) applied to consecutive sequences of prior daily rainfall map images. The results show that predictions for corner squares in the grid were less accurate than predictions obtained by a simple untrained classifier. However, SVM predictions for a central region outperformed the other two regions, as well as the untrained classifier. We conclude that there is some evidence that SVMs applied to large-scale precipitation maps can under some conditions give useful information for predicting regional rainfall, but care must be taken to avoid pitfalls.
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 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.
South African Sign Language Hand Shape and Orientation Recognition on Mobile Devices Using Deep Learning
(University of the Western Cape, 2017) Jacobs, Kurt; Ghaziasgar, Mehrdad; Venter, Isabella; Dodds, Reginald
In order to classify South African Sign Language as a signed gesture, five fundamental parameters need to be considered. These five parameters to be considered are: hand shape, hand orientation, hand motion, hand location and facial expressions. The research in this thesis will utilise Deep Learning techniques, specifically Convolutional Neural Networks, to recognise hand shapes in various hand orientations. The research will focus on two of the five fundamental parameters, i.e., recognising six South African Sign Language hand shapes for each of five different hand orientations. These hand shape and orientation combinations will be recognised by means of a video stream captured on a mobile device. The efficacy of Convolutional Neural Network for gesture recognition will be judged with respect to its classification accuracy and classification speed in both a desktop and embedded context. The research methodology employed to carry out the research was Design Science Research. Design Science Research refers to a set of analytical techniques and perspectives for performing research in the field of Information Systems and Computer Science. Design Science Research necessitates the design of an artefact and the analysis thereof in order to better understand its behaviour in the context of Information Systems or Computer Science.
A surface acoustic wave touchscreen-type device using two transducers
(2008) Ghaziasgar, Mehrdad; Connan, James
Current wireless human-computer interaction devices such as wireless mice and touchscreens, by and large, incorporate a sophisticated electronic architecture. The sophistication achieves wireless capabilities but carries over a cost overhead. In this paper we lay the foundation for developing a novel human-computer interaction device with reduced hardware sophistication. We developed a surface acoustic wave touchscreen-type device using only two transducers, as opposed to, typically, three or more transducers in conventional surface acoustic wave touchscreens. The transducers are mounted on a glass surface and connected into the line-in of a stereo sound card. User-initiated taps are detected, analysed and located on the surface, and the mouse cursor is moved to the computed screen location.
The use of mobile phones as service-delivery devices in sign language machine translation system
(University of the Western Cape, 2010) Ghaziasgar, Mehrdad; Connan, James; Dept. of Computer Science; Faculty of Science
This thesis investigates the use of mobile phones as service-delivery devices in a sign language machine translation system. Four sign language visualization methods were evaluated on mobile phones. Three of the methods were synthetic sign language visualization methods. Three factors were considered: the intelligibility of sign language, as rendered by the method; the power consumption; and the bandwidth usage associated with each method. The average intelligibility rate was 65%, with some methods achieving intelligibility rates of up to 92%. The average size was 162 KB and, on average, the power consumption increased to 180% of the idle state, across all methods. This research forms part of the Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL) project at the University of the Western Cape and serves as an integration platform for the group's research. In order to perform this research a machine translation system that uses mobile phones as service-delivery devices was developed as well as a 3D Avatar for mobile phones. It was concluded that mobile phones are suitable service-delivery platforms for sign language machine translation systems.

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