Search Results - (( emotion detection ((_ algorithm) OR (new algorithm)) ) OR ( wave optimization svm algorithm ))*

Dyslexia handwriting detection using Convolutional Neural Network (CNN) algorithm / Sofea Najihah Mohd Zaki by Mohd Zaki, Sofea Najihah

“…This project attempts to accurately detect the type of dyslexic handwriting. Convolutional Neural Network (CNN) algorithm was chosen as one of the possible solutions after a thorough analysis of many algorithms for dyslexic handwriting identification. …”
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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

“…This research demonstrates a new algorithmic method of moderating emotional content within Chinese dating reality shows based on cross-media analysis, combining text, audio, video, and social media feedback. …”
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The design for antrophomorphic sociable agent interaction through emotion detection by Ab. Aziz, Azizi, Siraj, Fadzilah, Sainin, Mohd Shamrie, Mohd Hassin, Mohd Hafiz

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Face emotion recognition using artificial intelligence techniques by Kartigayan Muthukaruppan

“…However, it is found that some emotion range overlaps with other emotion ranges. …”
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Real-time system for facial emotion detection using GPSO algorithm by Bashir, Mohammed Ghandi, R., Nagarajan, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

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Facial emotion detection using GPSO and Lucas-Kanade algorithms by Bashir, Mohammed Ghandi, Nagarajan, Ramachandran, Prof. Dr., Hazry, Desa, Prof. Madya Dr.

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

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Emotion Detection Based on EEG Signal by Mohamad Nasaruddin, Noradila

“…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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Characterization of digital intra-oral dental radiographs based on image enhancement algorithms (IEAs) / Siti Arpah Ahmad by Ahmad, Siti Arpah

“…The onset of digital imaging modality has made it possible to apply image processing algorithms to overcome the short comings of medical images such as noise, blurring and low contrast. …”
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Human Spontaneous Emotion Detection System by Radin Monawir, Radin Puteri Hazimah

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

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Characterization of digital intra-oral dental radiographs based on image enhancement algorithms (IEAs) / Siti Arpah Ahmad by Ahmad, Siti Arpah

“…The onset of digital imaging modality has made it possible to apply image processing algorithms to overcome the short comings of medical images such as noise, blurring and low contrast. …”
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Human faces: a review from detection to recognition by Mohamed Rizon, Mohamed Juhari, Muthukaruppan, Karthigayan, Sazali, Yaacob, Ramachandran, Nagarajan, Puteh, Saad, P. L. EhKan

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A Simple Yet Reliable Facial Emotion Detection For Campus Environment by Amar Lokman, Mus’ab Sahrim, Wan Zakiah Wan Ismail, Sharma Rao Balakrishnan, Juliza Jamaludin

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

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

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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

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Crypt Edge Detection Using PSO,Label Matrix And BI-Cubic Interpolation For Better Iris Recognition(PSOLB) by Hashim, Nurul Akmal

“…Iris identification is an automatic system to recognise an individual in biometric applications.Human iris is an internal organ that can be accessed from external view of the body.Moreover,the structure of the iris is formed in a complete random manner and has unique features such as crypts,furrows,collarets,pupil,freckles, and blotches.In fact, no iris patterns are the same.The iris structure is stable which it means the location of the iris features is permanent at certain point.Nevertheless,the shape of iris features changes slowly due to several factors which include aging,surgery,growth,emotion and dietary habits. Recently,there has been renewed interest in iris features detection.Gabor filter,cross entrophy, upport vector,and canny edge detection are methods which produce iris codes in binary codes representation.However,problems have occurred in iris recognition since low quality iris images are created due to blurriness,indoor or outdoor settings, and camera specifications.Failure was detected in 21% of the intra-class comparisons cases which were taken between intervals of three and six months intervals.However,the mismatch or False Rejection Rate (FRR) in iris recognition is still alarmingly high.Higher FRR also causes the value of Equal Error Rate (EER) to be high.The main reason for high values of FRR and EER is that there are changes in the iris due to the amount of light entering into the iris that changes the size of the unique features in the iris.One of the solutions to this problem is by finding any technique or algorithm to automatically detect the unique features.Therefore a new model is introduced which is called Crypt Edge Detection which combines PSO,Label Matrix,and Bi-Cubic Interpolation for Iris Recognition (PSOLB) to solve the problem of detection in iris features.In this research, the unique feature known as crypts has been chosen due to its accessibility and sustainability.Feature detection is performed using particle swarm optimisation (PSO) as an algorithm to select the best iris texture among the unique iris features by finding the pixel values according to the range of selected features.Meanwhile, label matrix will detect the edge of the crypt and the bi-cubic interpolation technique creates sharp and refined crypt images.In order to evaluate the proposed approach,FAR and FRR are measured using Chinese Academy of Sciences' Institute of Automation (CASIA) database for high quality images.For CASIA version 3 image databases, the crypt feature shows that the result of FRR is 21.83% and FAR is 78.17%.The finding from the experiment indicates that by using the PSOLB,the intersection between FAR and FRR produces the Equal Error Rate (EER) with 0.28%,which indicated that equal error rate is lower than previous value, which is 0.38%.Thus,there are advantages from using PSOLB as it has the ability to adapt with unique iris features and use information in iris template features to determine the user.The outcome of this new approach is to reduce the EER rates since lower EER rates can produce accurate detection of unique features.In conclusion,the contribution of PSOLB brings an innovation to the extraction process in the biometric technology and is beneficial to the communities.…”
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Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection by Lai Po Hung, Suraya Alias

“…Perhaps the next task to catch on is emotion detection, the task of identifying emotions. …”
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GPSO versus GA in facial emotion detection by Bashir, Mohammed Ghandi, Ramachandran, Nagarajan, Prof. Dr., Sazali, Yaacob, Prof. Dr., Hazry, Desa, Assoc. Prof. Dr.

Subjects: “…Emotion detection…”
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