scholarly journals Analysis of Human-Machine Interaction Through Facial Expression and Hand-Gesture Recognition

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1320-1328
Keyword(s):  
Facial Expression ◽  
Human Brain ◽  
Recent Work ◽  
Facial Expressions ◽  
Gesture Recognition ◽  
Oral Communication ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Human Machine Interaction ◽  
Machine Interaction

This paper focuses on a review of recent work on facial expression and hand gesture recognitions. Facial expressions and hand gestures are used to express emotions without oral communication. The human brain has the ability to identify the emotions of persons using expressions or hand gestures within a fraction of a second. Research has been conducted on human–machine interactions (HMIs), and the expectation is that systems based on such HMI algorithms should respond similarly. Furthermore, when a person intends to express emotions orally, he or she automatically uses complementary facial expressions and hand gestures. Extant systems are designed to express these emotions through HMIs without oral communication. Other systems have added various combinations of hand gestures and facial expressions as videos or images. The meaning or emotions conveyed by particular hand gestures and expressions are predefined in these cases. Accordingly, the systems were trained and tested. Further, certain extant systems have separately defined the meanings of such hand gestures and facial expressions

scholarly journals Komparasi Metode Klasifikasi untuk Deteksi Ekspresi Wajah Dengan Fitur Facial Landmark

2021 ◽  
Vol 8 (5) ◽  
pp. 949
Author(s):  
Fitra A. Bachtiar ◽  
Muhammad Wafi
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Cross Validation ◽  
Classification Model ◽  
Expression Recognition ◽  
Classification Methods ◽  
Human Machine Interaction ◽  
Facial Landmark ◽  
Total Data ◽  
Machine Interaction

<p><em>Human machine interaction</em>, khususnya pada <em>facial</em> <em>behavior</em> mulai banyak diperhatikan untuk dapat digunakan sebagai salah satu cara untuk personalisasi pengguna. Kombinasi ekstraksi fitur dengan metode klasifikasi dapat digunakan agar sebuah mesin dapat mengenali ekspresi wajah. Akan tetapi belum diketahui basis metode klasifikasi apa yang tepat untuk digunakan. Penelitian ini membandingkan tiga metode klasifikasi untuk melakukan klasifikasi ekspresi wajah. Dataset ekspresi wajah yang digunakan pada penelitian ini adalah JAFFE dataset dengan total 213 citra wajah yang menunjukkan 7 (tujuh) ekspresi wajah. Ekspresi wajah pada dataset tersebut yaitu <em>anger</em>, <em>disgust</em>, <em>fear</em>, <em>happy</em>, <em>neutral</em>, <em>sadness</em>, dan <em>surprised</em>. Facial Landmark digunakan sebagai ekstraksi fitur wajah. Model klasifikasi yang digunakan pada penelitian ini adalah ELM, SVM, dan <em>k</em>-NN. Masing masing model klasifikasi akan dicari nilai parameter terbaik dengan menggunakan 80% dari total data. 5- <em>fold</em> <em>cross-validation</em> digunakan untuk mencari parameter terbaik. Pengujian model dilakukan dengan 20% data dengan metode evaluasi akurasi, F1 Score, dan waktu komputasi. Nilai parameter terbaik pada ELM adalah menggunakan 40 hidden neuron, SVM dengan nilai  = 10<sup>5</sup> dan 200 iterasi, sedangkan untuk <em>k</em>-NN menggunakan 3 <em>k</em> tetangga. Hasil uji menggunakan parameter tersebut menunjukkan ELM merupakan algoritme terbaik diantara ketiga model klasifikasi tersebut. Akurasi dan F1 Score untuk klasifikasi ekspresi wajah untuk ELM mendapatkan nilai akurasi sebesar 0.76 dan F1 Score 0.76, sedangkan untuk waktu komputasi membutuhkan waktu 6.97´10<sup>-3</sup> detik.   </p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract">H<em>uman-machine interaction, especially facial behavior is considered to be use in user personalization. Feature extraction and classification model combinations can be used for a machine to understand the human facial expression. However, which classification base method should be used is not yet known. This study compares three classification methods for facial expression recognition. JAFFE dataset is used in this study with a total of 213 facial images which shows seven facial expressions. The seven facial expressions are anger, disgust, fear, happy, neutral, sadness, dan surprised. Facial Landmark is used as a facial component features. The classification model used in this study is ELM, SVM, and k-NN. The hyperparameter of each model is searched using 80% of the total data. 5-fold cross-validation is used to find the hyperparameter. The testing is done using 20% of the data and evaluated using accuracy, F1 Score, and computation time. The hyperparameter for ELM is 40 hidden neurons, SVM with  = 105 and 200 iteration, while k-NN used 3 k neighbors. The experiment results show that ELM outperforms other classification methods. The accuracy and F1 Score achieved by ELM is 0.76 and 0.76, respectively. Meanwhile, time computation takes 6.97 10<sup>-3</sup> seconds.      </em></p>

scholarly journals A Review of Emotion Recognition Based on EEG using DEAP Dataset

2021 ◽  
pp. 298-303
Author(s):  
Rama Chaudhary ◽  
Ram Avtar Jaswal
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Physiological Signals ◽  
Emotional States ◽  
Modern Time ◽  
Human Machine Interaction ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Machine Interaction

In modern time, the human-machine interaction technology has been developed so much for recognizing human emotional states depending on physiological signals. The emotional states of human can be recognized by using facial expressions, but sometimes it doesn’t give accurate results. For example, if we detect the accuracy of facial expression of sad person, then it will not give fully satisfied result because sad expression also include frustration, irritation, anger, etc. therefore, it will not be possible to determine the particular expression. Therefore, emotion recognition using Electroencephalogram (EEG), Electrocardiogram (ECG) has gained so much attraction because these are based on brain and heart signals respectively. So, after analyzing all the factors, it is decided to recognize emotional states based on EEG using DEAP Dataset. So that, the better accuracy can be achieved.

scholarly journals Facilitate sEMG-Based Human–Machine Interaction Through Channel Optimization

2019 ◽  
Vol 16 (04) ◽  
pp. 1941001 ◽  
Author(s):  
Zheng Wang ◽  
Yinfeng Fang ◽  
Gongfa Li ◽  
Honghai Liu
Keyword(s):  
Genetic Algorithm ◽  
Gesture Recognition ◽  
Degrees Of Freedom ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Machine Interaction ◽  
Prosthetic Hands ◽  
Channel Reduction ◽  
Machine Interaction ◽  
Channel Combination

Electromyography (EMG) has been widely accepted to interact with prosthetic hands, but still limited to using few channels for the control of few degrees of freedom. The use of more channels can improve the controllability, but it also increases system’s complexity and reduces its wearability. It is yet clear if optimizely placing the EMG channel could provide a feasible solution to this challenge. This study customized a genetic algorithm to optimize the number of channels and its position on the forearm in inter-day hand gesture recognition scenario. Our experimental results demonstrate that optimally selected 14 channels out of 16 can reach a peak inter-day hand gesture recognition accuracy at 72.3%, and optimally selecting 9 and 11 channels would reduce the performance by 3% and 10%. The cross-validation results also demonstrate that the optimally selected EMG channels from five subjects also work on the rest of the subjects, improving the accuracies by 3.09% and 4.5% in 9- and 11-channel combination, respectively. In sum, this study demonstrates the feasibility of channel reduction through genetic algorithm, and preliminary proves the significance of EMG channel optimization for human–machine interaction.

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