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  1. 1
    Emotion Detection Based on EEG Signal

    Emotion Detection Based on EEG Signal by Mohamad Nasaruddin, Noradila

    Published 2021
    “…Thus, this project aimed to study the emotion detection through EEG signal and proposed the right algorithm to process the signal. …”
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    Final Year Project
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  2. 2
    Development of a personified face emotion recognition technique using fitness function

    Development of a personified face emotion recognition technique using fitness function by Muthukaruppan, Karthigayan, Mohd Rizon, Mohamed Juhari, Ramachandran, Nagarajan, Masanori, Sugisaka, Sazali, Yaacob, Mohd Rozailan, Mamat, Hazry, Desa

    Published 2009
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  3. 3
    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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  4. 4
    Human faces: a review from detection to recognition

    Human faces: a review from detection to recognition by Mohamed Rizon, Mohamed Juhari, Muthukaruppan, Karthigayan, Sazali, Yaacob, Ramachandran, Nagarajan, Puteh, Saad, P. L. EhKan

    Published 2009
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  5. 5
    Various Recognition approach in human faces

    Various Recognition approach in human faces by Karthigayan, M., Mohd Rizon, Mohamed Juhari, Sazali, Yaacob, Nagarajan, R., Puteh, Saad, Muhammed Firdaus, Hashim, Haniza, Yazid

    Published 2009
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  6. 6
    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs

    Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

    Published 2021
    “…Firstly, it aims to use both linear and nonlinear features of EEG signals to identify emotional influences on gender behavior. …”
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  7. 7
    Deep learning metaphor detection with emotion-cognition association

    Deep learning metaphor detection with emotion-cognition association by Razali, Md Saifullah, Abdul Halin, Alfian, Chow, Yang-Wai, Mohd Norowi, Noris, Doraisamy, Shyamala

    Published 2022
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  8. 8
    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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  9. 9
    Affective computation on EEG correlates of emotion from musical and vocal stimuli

    Affective computation on EEG correlates of emotion from musical and vocal stimuli by Khosrowabadi, Reza, Abdul Rahman, Abdul Wahab, Ang, Kai Keng, H Baniasad, Mohammad.

    Published 2009
    “…A classification algorithm is subsequently used to learn and classify the extracted EEG features. …”
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    Proceeding Paper
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  10. 10
    Eye fixation versus pupil diameter as eye- tracking features for virtual reality emotion classification

    Eye fixation versus pupil diameter as eye- tracking features for virtual reality emotion classification by Lim Jia Zheng, James Mountstephens, Jason Teo

    Published 2022
    “…For four-quadrant emotion recognition, eye fixation as a learning feature produces better classification accuracy compared to pupil diameter. …”
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  11. 11
    EMOTION RECOGNITION USING GALVANIC SKIN RESPONSE (GSR) SIGNAL

    EMOTION RECOGNITION USING GALVANIC SKIN RESPONSE (GSR) SIGNAL by RAMOS UKAR, YAKOBUS

    Published 2022
    “…Moreover, the obtained dataset must be suitable for machine learning algorithms. Acquired results may help select proper GSR signals with emotional labels for further dataset pre-processing and feature extraction.…”
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    Final Year Project Report / IMRAD
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  12. 12
    Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm

    Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Md. Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

    Published 2019
    “…Furthermore, the right and left occipital channels may help in identifying happiness, sadness, surprise and neutral emotional states. The DEFS_Ch algorithm raised the linear discriminant analysis (LDA) classification accuracy from 80% to 86.85%, indicating that DEFS_Ch may offer a useful way for reliable enhancement of the detection of different emotional states of the brain regions.…”
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  13. 13
    Speech emotion verification system (SEVS) based on MFCC for real time applications

    Speech emotion verification system (SEVS) based on MFCC for real time applications by Kamaruddin, Norhaslinda, Abdul Rahman, Abdul Wahab

    Published 2008
    “…Experimental results show potential of using these techniques to detect and distinguish three basic emotions from speech for real-time applications based on features extracted using MFCC.…”
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  14. 14
    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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  15. 15
    Deep learning based emotion recognition for image and video signals: matlab implementation

    Deep learning based emotion recognition for image and video signals: matlab implementation by Ashraf, Arselan, Gunawan, Teddy Surya, Kartiwi, Mira

    Published 2021
    “…Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. …”
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    Book
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  16. 16
    An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition

    An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition by Al-Qammaz, Abdullah Yousef Awwad

    Published 2019
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  17. 17
    Algorithms for moderating effect of emotional value from a cross-media data fusion perspective: a case study of Chinese dating reality shows

    Algorithms for moderating effect of emotional value from a cross-media data fusion perspective: a case study of Chinese dating reality shows by Zhang, Shasha, Dong, Qiming, Yasin, Megat Al Imran, Fang, Ng Chwee

    Published 2026
    “…The above are the major components of a vision system: Data Acquisition, Multimodal Preprocessing, Cross-Media Feature Extraction, Emotional Value Detection and Moderation, and Interpretability and Visualization. …”
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  18. 18
    RSA Encryption & Decryption using JAVA

    RSA Encryption & Decryption using JAVA by Ramli, Marliyana

    Published 2006
    “…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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  19. 19
    Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian

    Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian by Saffian, Norhafizah

    Published 2017
    “…The facial expression consists of three steps that are face detection, facial feature extraction, and classification of feature extraction. …”
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  20. 20
    Performance analysis for facial expression recognition under salt and pepper noise with median filter approach

    Performance analysis for facial expression recognition under salt and pepper noise with median filter approach by Idris, Azrini

    Published 2013
    “…In face recognition, the simple process of face recognition system should go through image data retrieval, face detection, facial feature extraction and face recognition. …”
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