Search Results - (( emotion detection system algorithm ) OR ( _ classification based algorithm ))
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Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian
Published 2017“…Based on this calculation, the accuracy of this algorithm is 93% using the k values of 5. …”
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Learner’s emotion prediction using production rules classification algorithm through brain computer interface tool
Published 2018“…From the data analysis using WEKA software, the production rules classifier (PART) is found to be the most accurate classification algorithm in classifying the emotion which yields the highest precision percentage of 99.6% compared to J48 (99.5%) and Naïve Bayes (96.2%). …”
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Eye fixation versus pupil diameter as eye- tracking features for virtual reality emotion classification
Published 2022“…Therefore, this empirical study has shown that eyetracking- based emotion recognition systems would benefit from using features based on eye fixation data rather than pupil size.…”
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Proceedings -
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Human emotion classifications for automotive driver using skin conductance response signal
Published 2016“…The video clip stimulus method showed 95.7% efficacy in detecting happiness and anger. The affective assessment classification rate obtained from SCR processing was more than 70% accuracy based on the off-line support vector machine classifier-processing algorithm. …”
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Conference or Workshop Item -
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Vader lexicon and support vector machine algorithm to detect customer sentiment orientation
Published 2023“…Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. …”
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Facial geometry and speech analysis for depression detection
Published 2017“…The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. …”
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A review of chewing detection for automated dietary monitoring
Published 2022“…The decision tree approach was more robust and its classification accuracy (75%–93.3%) was higher than those of the Viterbi algorithm-based finite-state grammar approach, which yielded 26%–97% classification accuracy. …”
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Real-time system for facial emotion detection using GPSO algorithm
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Facial emotion detection using GPSO and Lucas-Kanade algorithms
Published 2010Get full text
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Human Spontaneous Emotion Detection System
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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Facial emotion detection using Guided Particle Swarm Optimization (GPSO)
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Stress Level Detection using Fuzzy Logic / Nor Husna Nabila Khuzaini
Published 2020“…This fuzzy logic system were successful develop. The result indicate in stress level detection based on fuzzy logic system that provide the symptom of stress and the facial emotion. …”
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Detecting emotions and depression through voice
Published 2021“…A deep learning algorithm can detect emotion, including depression, using a voice signal. …”
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