Hasimah Ali
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Preferred name
Hasimah Ali
Official Name
Ali, Hasimah
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Ali, H.
Ali, H
Ali, Hashimah
Ali, H. I.
Bt Ali, Hasimah
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Scopus Author ID
57218540740
Researcher ID
EKZ-6160-2022

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Investigation of nonlinear feature extraction techniques for facial emotion recognition

2016 , Hasimah Ali

Over the last decades, facial emotion recognition has received a significant interest among researchers in areas of computer vision, pattern recognition and its related field. The increasing applications of facial emotion recognition have shown a sizeable impact in many areas ranging from psychology to human-computer interaction (HCI). Although facial emotion recognition has achieved a certain level of success, however its performance is far from human perception. Many approaches have been constantly proposed in the literature. In fact, the ability of facial emotion recognition to operate in fully automated with high accuracy remains challenging due to various problems such as intra-class variations, inter-class similarities and subtle changes of facial features. The adhered problem is further hampered as physiognomies of faces with respect to age, ethnicity and gender, thus increase the difficulties of recognizing the facial emotion. In order to resolve this problem, this thesis aims to develop nonlinear features extraction techniques of using Higher Order Spectra (HOS) and Empirical Mode Decomposition (EMD) separately in recognizing the seven facial emotions (anger, disgust, fear, happiness, neutral, sadness and surprise) from static images. A pre-processing step of isolating face region from different background was first employed by means of face detection. The 2-D facial image was then projected into 1-D facial signal by successive projection via Radon transform. Radon transform is translation and rotation invariant, hence preserves the variations in pixel intensities. The facial signal that describes the expression was extracted using HOS and EMD to obtain a set of significant features. In HOS framework, the third order statistic or bispectrum that captures contour (shape) and texture information was applied on facial signal. In this work, a new set of bispectral features was used to characterize the distinctive features of seven classes of emotion. While, in EMD framework, the facial signal was decomposed using EMD to produce a small set of intrinsic mode functions (IMFs) via sifting process. The IMF features which exhibit the unique pattern were used to differentiate the facial emotions.

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A cascade hyperbolic recognition of buried objects using hybrid feature extraction in ground penetrating radar images

2021-08-27 , Hasimah Ali , Ahmad Firdaus Ahmad Zaidi , Wan Khairunizam Wan Ahmad , Mohd Shuhanaz Zanar Azalan , Tengku Amran T.S. , Ahmad M.R. , Mohamed Elshaikh Elobaid Said Ahmed

Ground penetrating radar (GPR) has been acknowledged as effective nondestructive technique for imaging the subsurface. But the process of recognizing hyperbolic pattern of buried objects is subjective and mainly relies upon operator's knowledge and experience. This project proposed a hyperbolic recognition of buried objects using hybrid feature extraction in GPR subsurface mapping. In this framework, a cascade hyperbolic recognition by means of Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) are used as hybrid feature recognizing hyperbolic of buried objects. The rationale for an initial focus on cascade hyperbolic recognition is motivated by unique features exhibits by EMD and DWT behaviour in characterizing the hyperbolic pattern which make them particularly well suited to utilities detection in GPR. A series of experiments has been conducted on hyperbolic pattern based on hybrid features using four different geometrical shapes of cubic, cylindrical disc and spherical. Based on the results obtained, the hybrid features of IMF1+ wavelet transform (cH1) shows promising recognition rate in recognizing the hyperbolic that having different geometrical shapes of buried objects.

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Fuzzy Logic Cascaded Current Control of DC Motor Variable Speed Drive using dSPACE

2023-01-01 , Ahmad Firdaus Ahmad Zaidi , Ni L.P. , Leong Jenn Hwai , Jenn , Syahrul Ashikin Azmi , Kamarulzaman Kamarudin , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Siti Nurul Aqmariah Mohd Kanafiah , Jusman Y.

Two-wheel e-scooter falls under low power segment for Battery Electric Vehicle (BEV) and has gain more popularity in urban commuting. Most entry level e-scooter is still powered by DC motor due to low cost and ease of control. However basic open-loop DC Motor control employed through throttling is plugged with limited efficiency, precision, and range of speed control. Closed-loop control enables real time adjustment according to preset speed which becomes handy during auto cruising. To ensure good dynamic response, improved robustness and stable wide speed control range, a good control scheme for the motor is essential. In this project, a variable speed control scheme, namely fuzzy logic cascaded current control system was designed using MATLAB Simulink, comprising speed control loop and a current control loop 185 W Separately Excited Brushed DC Motor. The proposed control system was tested on hardware using dSPACE DS1104 platform. The system's output speed is obtained using an incremental encoder, while the output current is measured with a current sensor. Subsequently, the control system's stability, robustness, and dynamic performance were evaluated by driving the system on 120 W electrical load at varying speed. The system performance has proved superior to closed-loop by 70% on low speed ripple reduction and is on par with PI cascaded current control scheme.

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Assessment of Control Drive Technologies for Induction Motor: Industrial Application to Electric Vehicle

2021-06-11 , Ahmad Firdaus Ahmad Zaidi , Syahrul Ashikin Azmi , Kamarulzaman Kamarudin , Leong Jenn Hwai , Jenn , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Zamri Che Mat Kasa

Nowadays electric vehicle has increasingly gained much popularity indicated by growing global share market targeted at 30% by 2030 after recording 7.2million global stock in 2019. Compared to Internal Combustion Engine (ICE) counterpart, Battery Electric Vehicles (BEV) produce zero tailpipe emission which greatly reducing carbon footprints. Induction motor has been widely used and its control technology has evolved from scalar type volt/hertz to recent predictive control technology. This allows induction motor's application to expand from being the workhorse of industry to become prime mover in electric vehicle, where high performance is expected. Among vector control scheme, Direct Torque Control (DTC) has gained interest over Field Oriented Control (FOC) with simpler structure, better robustness and dynamics performance yet suffer from high torque and flux ripple. In electric vehicle applications, high ripple at low speed is highly undesirable, potentially causing torsional vibration. High performance control requires speed sensor integration, which often increase complexity in the design. The work aims to review the best control technology for induction motor in electric vehicle application through performance parameter evaluation such as improvement on dynamic response, torque and flux ripple reduction, and component optimization. Several arise issues in motor control and possible methods to circumvent are highlighted in this work. In conclusion, model predictive torque control (MPTC) is the most promising scheme for electric vehicle with excellent dynamic response, good low speed performance, and 50% torque ripple reduction compared to conventional DTC and potential integration with sliding mode observer for sensorless solution.

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Hyperbolic detection of ground penetrating radar for buried pipes utilities using Viola Jones

2025-06 , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Nurul Syahirah Mohd Ideris , Tengku Sarah Tengku Amran

GPR (Ground Penetrating Radar) is well-known as an effective non-invasive imaging approach for shallow nature underground discovery, like finding and locating submerged objects. Although GPR has achieved some success, it is difficult to automatically process GPR images because human experts must interpret GPR images of buried objects. This can happen due to the possibility of a variety of mediums or underground noises from the environment, especially rocks and roots of trees. Thus, detecting hyperbolic echo characteristics is critical. As a result, Viola Jones detection is used to determine whether the presence of a hyperbolic signature underground indicates a pipe or not. GPR can also be used in the public works department because it is a non destructive tool. Workers, for example, should be aware of the pipe size that must be replaced when it leaks. The original GPR image already shows hyperbolic image distortion due to pipe refraction. The current method is unreliable due to its lack of flexibility. As a result, there is another method for resolving this issue. Thus, the image will be pre-processed to eliminate or reduce background noise in the GPR input image. The results of this project demonstrate that the Viola Jones algorithm can accurately detect hyperbolic patterns in GPR images.

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Illumination Effects on Facial Expression Recognition using Empirical Mode Decomposition

2024-01-01 , Hasimah Ali , Wan Khairunizam Wan Ahmad , Muthusamy H. , Mohamed Elshaikh Elobaid Said Ahmed

Facial expression recognition (FER) has been acknowledged as a significant modality that could bring facial expression into human-machine interaction and make the interaction more efficient. However, the ability of FER tope rate in a fully automated and robust manner is still challenging. Illumination effects, for example, make the facial expression images always contaminated with different levels of ambient noise (such as brightness variation) in acluttered background. Thus, this paper aims to investigate the illumination effects (brightness variations) on facial expression recognition using empirical mode decomposition reconstruction techniques. In this framework, firstly, the noisy facial expression images were simulated with the illumination effects using different brightness levels of 30%,40%, 50%, 60%, and 70%. Then, the EMD will decompose the noisy facial expression images into a small set of intrinsic mode functions (IMF), namely IMF1, IMF2, IMF3, and residue. Based on property held by EMD, the signals are decomposed into several IMF components, each with a different time scale. Because the last several IMFs represent the majority of illumination effects, various reconstruction techniques for IMFs have been investigated atvarious brightness levels. Feature reduction techniques Principal component analysis (PCA) and linear discriminant analysis (LDA) have been employed to reduce the high-dimensional space of IMF features into low-dimensional IMF features. The reduced IMF reconstructions were then used as input to the k-nearest neighbour classifier to recognise the seven facial expressions. A series of experiments have been conducted on the JAFEE database using various reconstruction IMFs together with PCA plus LDA. Based on the results obtained, the reconstruction of IMF1 + IMF2+ IMF3 shows the highest accuracy in high illumination conditions, which is 99.06%.

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3D Reconstruction of embedded object using ground penetrating radar

2023-01-01 , Fadil N.D. , Hasimah Ali , Ahmad Firdaus Ahmad Zaidi , Kamal W.H.B.W. , Basri N.A.M.

Ground Penetrating Radar (GPR) is a non-destructive device widely used to locate and map underground utilities such as pipes, cables, etc. Its principle is based on the reflection signal of a transmitter-receiver antenna that strikes underground objects by means of the propagation of a short pulse of electromagnetic waves into the ground. The GPR will produce a hyperbolic curve as a result of the object's presence. Accurate interpretation of hyperbola curves is greatly important and highly depends on user expertise; thus, it is considered a challenge. To address this issue, this study aims to develop 3D reconstructions of embedded objects. In this study, C-scan images were acquired, and 3D interpolation and the Synthetic Aperture Focusing Technique (SAFT) were introduced. In this framework, the acquired data is subjected to pre-processing techniques via time-zero correction, background removal using average background subtraction, and Kirchoff's migration method. The software Reflex 3D Scan has been used to analyse and preprocess the 3D reconstruction of embedded objects. The obtained results show that 3D interpolation and SAFT methods are not only able to reconstruct 3D models but are also able to reveal information on the dimension and location of the buried object represented by voxel points in the 3D space cube.

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Shape Recognition of GPR Images using Hough Transform and PCA plus LDA

2022-01-01 , Hasimah Ali , Norfatihah Bahari , Mohamed Elshaikh Elobaid Said Ahmed , Siti Nurul Aqmariah Mohd Kanafiah , Amran T.S.T. , Jusman Y.

Ground penetrating radar (GPR) is a nondestructive test used for shallow subsurface investigation such as land mine detection, mapping and locating buried utilities. In practical applications, GPR images could be noisy due to system noise, the heterogeneity of the medium, and mutual wave interactions. Hence, it is a complex task to recognize the hyperbolic pattern from GPR B-scan images. Thus, this project proposes combined shape recognition of buried objects using Hough Transform (HT) and PCA plus LDA in GPR images. The use of HT is justified because it has the property of transforming global curve detection into efficient peak detection in the Hough parameter space. Whereas PCA plus LDA tries to maximize between-class scatter while minimizing within-class scatter. In this framework, the preprocessed GPR images were extracted using HT. The extracted HT features were subjected to PCA plus LDA to map them from high into lower dimensional features. Then, the reduced PCA+LDA features were used as input to the k-NN classifier to recognize four geometrical shapes cubic, disc, and spherical of the buried objects. Based on the results obtained, the average recognition rate of reduced HT features using PCA plus LDA was achieved 85.30% thus shows a promising result.

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Classification of Body Mass Index Based Facial Images using Empirical Mode Decomposition

2021-06-11 , Hasimah Ali , Yee, O.S. , Wan Khairunizam Wan Ahmad

Human faces contain rich information. Recent studies found that facial features have relation with human weight or body mass index (BMI). Decoding "facial information"from the face in predicting the BMI could be linked to the various health marker. This paper proposed the classification of body mass index (BMI) based on appearance based features of facial images using empirical mode decomposition (EMD) as feature extraction technique. The facial images that describe the body mass index was extracted using EMD to obtain a set of significant features. In this framework, the facial image was decomposed using EMD to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF features which exhibit the unique pattern were used to classify the BMI. The obtained features were then fed into machine learning classifier such as k-nearest neighbour and support vector machines (SVM) to classify the three BMI classes namely normal, overweight and obese. The obtained results show that the IMF2 feature using SVM classifier achieved recognition rate of 99.12% which show promising result.

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Feature Extraction based on Empirical Mode Decomposition for Shapes Recognition of Buried Objects by Ground Penetrating Radar

2021-06-11 , Hasimah Ali , Mohd Shuhanaz Zanar Azalan , Zaidi A.F.A. , Amran T.S.T. , Ahmad M.R. , Elshaikh M.

Ground penetrating radar (GPR) is one of the promising non-destructive imaging tools investigations for shallow subsurface exploration such as locating and mapping the buried utilities. In practical applications, GPR images could be noisy due to the system noise, the heterogeneity of the medium, and mutual wave interactions thus, it is a complex task to recognizing the hyperbolic signature of buried objects from GPR images. Therefore, this paper aims to develop nonlinear feature extraction technique of using Empirical Mode Decomposition (EMD) in recognizing the four geometrical shapes (cubic, cylindrical, disc and spherical) from GPR images. A pre-processing step of isolating hyperbolic signature from different background was first employed by mean of Region of Interest (ROI). The hyperbolic signature that describes the shapes was extracted using EMD decomposition to obtain a set of significant features. In this framework, the hyperbolic pattern was decomposed of using EMD, to produce a small set of intrinsic mode functions (IMF) via sifting process. The IMF properties of the signature that exhibit the unique pattern was used as potential features to differentiate the geometrical shapes of buried objects. The extracted IMF features were then fed into machine learning classifier namely Support Vector Machines. To evaluate the effectiveness of the proposed method, a set data collection of GPR-images has been acquired. The experimental results show that the recognition rate of using IMF features was achieved 99.12% accuracy in recognizing the shapes of buried objects whose shows the promising result.

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