Wan Khairunizam Wan Ahmad
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Wan Khairunizam Wan Ahmad
Official Name
Wan Khairunizam, Wan Ahmad
Alternative Name
Wan, Khairunizam
Ahmad, Wan Khairunizam Wan
Khairunizam, W. A. N.
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Scopus Author ID
57200576499
Researcher ID
E-6072-2011

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  • Publication
    Virtual Markers based Facial Emotion Recognition using ELM and PNN Classifiers
    ( 2020-02-01)
    Murugappan M.
    ;
    Maruthapillai V.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Mutawa A.M.
    ;
    Sruthi S.
    ;
    Yean C.W.
    Detecting different types of emotional expressions from the subject's face is important for developing intelligent systems for a variety of applications. This present work proposed virtual markers based on Facial emotion expression recognition using the Extreme Learning Machine (ELM) and Probabilistic Neural Network (PNN). A facial emotional expression database is developed with 55 undergraduate university students (male: 35, female: 20) of age range between 20-25 years with a mean age of 23.9 years. A HD webcam is used to capture the facial image and Haar Like features and Ada Boost classifier is used to detect the face and eyes through Open CV. A mathematical model based is used to place ten virtual markers called Action Units (AUs) on subjects face at a defined location. Later, Lucas-kanade optical flow algorithm is used to track the marker movement while the subject expressing different emotions and the distance between the center of the face to each marker is used as a feature for classifying emotions. One way Analysis of Variance (ANOVA) is used to test the statistical significance of the features and five fold cross-validation method is used to input the feature for classifiers. In this work, two non-linear classifiers namely, ELM and PNN are used for emotional expression classification. The experimental results give a maximum mean emotion classification rate of 88% and 92% in ELM and PNN classifiers, respectively. Maximum individual class accuracy of happiness-96%, surprise-94%, anger-92%, sadness-88%, disgust-90% and fear 89% is achieved using PNN. The experimental results confirm that the proposed system is able to distinguish six different emotional expressions and could be used as a potential tool for a variety of applications which include, e-learning, pain assessment, psychological counseling, human-machine interaction-based applications, etc.
  • Publication
    Emotion Classification in Parkinson's Disease EEG using RQA and ELM
    ( 2020-02-01)
    Murugappan M.
    ;
    Alshuaib W.B.
    ;
    Bourisly A.
    ;
    Sruthi S.
    ;
    Wan Khairunizam Wan Ahmad
    ;
    Shalini B.
    ;
    Yean W.
    Most of the earlier works focused on diagnosing the Parkinson's Disease (PD) through behavioral measures. Very few researches attempted to identify the emotion impairment in PD through EEG signals. The main objective of this work is to classify the emotions perceived by the PD subjects through audio-visual stimuli using Electroencephalogram (EEG) signals. EEG database of 20 subjects on PD and 20 NC is developed using a 14 channel wireless EEG device at a sampling frequency of 128 Hz. Audiovisual stimuli of six emotions (happiness, sadness, anger, fear, surprise, and disgust) are used to induce the emotions. The acquired EEG signals are pre-processed using the IIR Butterworth filter to remove the noises, artifacts, and interferences in EEG signals and used to derive three frequency bands (alpha, beta and gamma) of EEG data. Recurrence Quantification Analysis (RQA) is used to extract the two most significant features (Maximum Line Length, Maximum Vertical Line Length) from Recurrence Plot (RP). Besides, these features are combined together called ALL features. Therefore, three types of features were tested using one-way analysis of variance (ANOV A) to test its significance in classifying emotions in PD and NC and a five-fold cross-validation method is used to split the features into training and testing set. Finally, the Extreme Learning Machine (ELM) classifier with two different types of kernel functions used to classify the emotions of PD and NC. The maximum mean accuracy of 89.17%, 84.50% is achieved on NC and PD, respectively. The maximum individual class accuracy of NC/PD is sadness-90.90/87.50, happiness-91.10/84.30, fear-88/84.10, disgust-88.5/82.70, surprise-87.4/84.60, and anger-89.10/83.80. Experimental results indicate that RQA features are highly useful in detecting the emotions in PD compared to other methods and ELM gives the highest mean accuracy compared to other works in the literature.