Isolating Uncertainty of the Face Expression Recognition with the Meta-Learning Supervisor Neural Network

2021 ◽  
Author(s):  
Stanislav Selitskiy ◽  
Nikolaos Christou ◽  
Natalya Selitskaya
Keyword(s):  
Neural Network ◽  
Expression Recognition ◽  
Face Expression ◽  
Meta Learning ◽  
The Face ◽  
Face Expression Recognition

scholarly journals Emotion Recognition from Facial Expression Using Machine Vision Approach

2020 ◽  
pp. 35-40 ◽  
Keyword(s):  
Machine Vision ◽  
Median Filter ◽  
Noise Removal ◽  
Expression Recognition ◽  
Statistical Features ◽  
Face Expression ◽  
Section Technique ◽  
The Face ◽  
Face Expression Recognition ◽  
Binary Features

In this study emotion-based face expression recognition framework has been proposed using a machine vision (MV) approach. The face emotion dataset has been collected local survey in Bahawalpur city dataset divide into 3 classes happy, sad, and angry. A total of 600 images of size (256 x 256) were transformed into a gray level format and employed a median filter for noise removal. Three non-overlapping regions of interest (ROIs) of size (50 x 50) have been taken and analyze 1800 (600 x 3) ROIs on the overall dataset. Total 45 Statistical features named as texture, histogram, and binary features were extracted. Select optimize features using the correlation-based feature section technique. The optimized dataset employed of MV classifiers namely random forest (RF), logistic (Lg), and J48 are obtained very promising accuracy 96.33%, 95.67%, and 95.33% respectively.

scholarly journals Pengenalan Ekspresi Wajah Menggunakan DCT dan LDA untuk Aplikasi Pemutar Musik (MOODSIC)

2018 ◽  
Vol 5 (5) ◽  
pp. 559 ◽  
Author(s):  
I Gede Pasek Suta Wijaya ◽  
Asno Azzawagaam Firdaus ◽  
Aditya Perwira Joan Dwitama ◽  
Mustiari Mustiari
Keyword(s):  
Real Time ◽  
Modern Society ◽  
Expression Recognition ◽  
Statistical Classification ◽  
Recognition Error ◽  
Statistical Classifier ◽  
Face Expression ◽  
The Face ◽  
Face Expression Recognition ◽  
Offline Testing

<p>Masyarakat modern dengan kesibukan sehari-harinya tentu akan mendapat tekanan emosional yang cukup tinggi. Hal yang dilakukan untuk meredakan emosi tersebut adalah salah satu dengan mendengarkan musik. MOODSIC merupakan sebuah aplikasi yang dapat memutar musik sesuai dengan ekspresi wajah pengguna. Aplikasi MOODSIC dibangun menggunakan mesin pengenalan ekspres wajah berbasis DCT dan LDA serta algoritma klasifikasi statistik. Berdasarkan hasil pengujian secara <em>off-line</em> mesin pengenalan ekspresi wajah berhasil memberikan performa yang baik, dengan akurasi sebesar 100% untuk data masukkan terdiri atas fitur DCT 144 elemen, 6 eigen vektor LDA dan klasifikasi statistik jenis LDA. Mesin pengenalan ekspresi wajah memerlukan waktu pengenalan yang pendek yaitu 1 milidetik. Secara <em>real-time</em> MOODSIC memberikan hasil yang cukup baik dengan akurasi pengenalan ekspresi sebesar 91.51% atau dengan tingkat kesalahan pengenalan 9.49%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em><strong></strong></em><em>Modern society lifestyles face many activities every day, which make people receive a fairly high emotional stress. To reduce such kind of emotions can be treated by listening music. MOODSIC is an application that can play music according to the user's face expression. MOODSIC is developed using face expression recognition machine based on DCT, LDA and statistical classification algorithm. Based on offline testing result, face expression recognition machine successfully give good performance with accuracy of 100% when DCT features are 144 elements, 6 eigen vectors of LDA and kind of statistical classifier is LDA. The face expression recognition engine took shorter time to classification about 1 milliseconds. MOODSIC also give good performance with the accuracy of expression recognition about 91.51% or recognition error of 9,49% for real-time testing.</em></p>

scholarly journals Face Expression Recognition using CNN

2021 ◽  
pp. 235-240
Author(s):  
Ashna Namira ◽  
Aravind Naik ◽  
Nikhil Floyd Dsouza
Keyword(s):  
Neural Network ◽  
Psychological Theory ◽  
Machine Learning Algorithms ◽  
Expression Recognition ◽  
Emotional States ◽  
Face Expression ◽  
Face Images ◽  
The Face ◽  
Complex Features ◽  
Speed Training

The emotions evolved in face have an excellent influence on decisions and arguments about various subjects. In psychological theory, emotional states of an individual are often classified into six main categories: surprise, fear, disgust, anger, happiness and sadness. Automatic extraction of those emotions from the face images can help in human computer interaction also as many other applications. Machine learning algorithms and particularly deep neural network can learn complex features and classify the extracted patterns. In this paper, a deep learning¬based framework is used for human emotion recognition. The proposed framework uses the feature extraction then a Convolutional Neural Network (CNN) for classification. The experimental results show that the proposed methodology increases both of the speed training process of CNN and therefore the recognition accuracy.

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