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  1. 1
    Particle Swarm Optimization algorithm for facial emotion detection

    Particle Swarm Optimization algorithm for facial emotion detection by Ghandi, B.M., Nagarajan, R., Hazry, Desa, Prof. Madya Dr.

    Published 2010
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  2. 2
    Human Spontaneous Emotion Detection System

    Human Spontaneous Emotion Detection System by Radin Monawir, Radin Puteri Hazimah

    Published 2018
    “…Having smart computerized system which can understand and instantly gives appropriate response to human is the utmost motive in human and computer interaction (HCI) field.It is argued either HCI is considered advance if human could not have natural and comfortable interaction like human to human interaction.Besides,despite of several studies regarding emotion detection system, current system mostly tested in laboratory environment and using mimic emotion.Realizing the current system research lack of real life or genuine emotion input,this research work comes up with the idea of developing a system that able to recognize human emotion through facial expression.Therefore,the aims of this study are threefold which are to enhance the algorithm to detect spontaneous emotion,to develop spontaneous facial expression database and to verify the algorithm performance.This project used Matlab programming language,specifically Viola Jones method for features tracking and extraction,then pattern matching for emotion classification purpose.Mouth feature is used as main features to identify the emotion of the expression.For verification purpose,the mimic and spontaneous database which are obtained from internet,open source database or novel (own) developed databases are used.Basically,the performance of the system is indicated by emotion detection rate and average execution time.At the end of this study,it is found that this system is suitable for recognizing spontaneous facial expression (63.28%) compared to posed facial expression (51.46%).The verification even better for positive emotion with 71.02% detection rate compared to 48.09% for negative emotion detection rate.Finally,overall detection rate of 61.20% is considered good since this system can execute result within 3s and use spontaneous input data which known as highly susceptible to noise.…”
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  3. 3
    Emotion detecting humanoid – the use of GPSO Algorithm

    Emotion detecting humanoid – the use of GPSO Algorithm by Bashir, Mohammed Ghandi, Ramachandran, Nagarajan, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

    Published 2012
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  4. 4
    Real-time system for facial emotion detection using GPSO algorithm

    Real-time system for facial emotion detection using GPSO algorithm by Bashir, Mohammed Ghandi, R., Nagarajan, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

    Published 2012
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  5. 5
    Facial emotion detection using Guided Particle Swarm Optimization (GPSO)

    Facial emotion detection using Guided Particle Swarm Optimization (GPSO) by Bashir, Mohammed Ghandi, Nagarajan, R., Hazry, Desa

    Published 2009
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  6. 6
    The Determination of Pile Capacity Using Artificial Neural-net: An Optimization Approach

    The Determination of Pile Capacity Using Artificial Neural-net: An Optimization Approach by Ab. Malik, Rosely, Jamil S., Mohamed

    Published 2001
    “…Using the developed algorithm, the safety measures involved are such as reliability index and the probability of failure; instead of only factor of safety if conventional deterministic approach is used. …”
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  7. 7
    The design for antrophomorphic sociable agent interaction through emotion detection

    The design for antrophomorphic sociable agent interaction through emotion detection by Ab. Aziz, Azizi, Siraj, Fadzilah, Sainin, Mohd Shamrie, Mohd Hassin, Mohd Hafiz

    Published 2003
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  8. 8
    GLCM correlation approach for blood vessel identification in thermal image

    GLCM correlation approach for blood vessel identification in thermal image by Rusli, Nazreen, Md Yusof, Hazlina, Sidek, Shahrul Na'im, Ishak, Nor Izzati

    Published 2019
    “…The maturity of detection in emotions via thermal camera is evolving recently since it is able to detect the “hot” parts of human face composition replicating the area of blood vessels. …”
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  9. 9
    Multiview face emotion recognition using geometrical and texture features

    Multiview face emotion recognition using geometrical and texture features by Goodarzi, Farhad

    Published 2017
    “…A 3D face pose estimation algorithm detects head rotations of Yaw, Roll and Pitch for emotion recognition. …”
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  10. 10
    Discriminative feature representation for Malay children’s speech recognition / Seyedmostafa Mirhassani

    Discriminative feature representation for Malay children’s speech recognition / Seyedmostafa Mirhassani by Mirhassani, Seyedmostafa

    Published 2015
    “…In case of multiple filterbanks the cepstral features are used in different experts for performing classification based on different representation of speeches. …”
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