Feature Extraction Trends for Intelligent Facial Expression Recognition: A Survey

Azam, Irfan; Khan, Sajid Ali

doi:10.31449/inf.v42i4.2037

Cited by 7 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the feature extraction step is done [22, 23 24]. In this step, discriminative information about each face is stored in a compact feature vector [25]. After, there is a learning or modeling step wherein a machine learning algorithm is used to fit a model of the appearance of the faces in the gallery.…”

Section: Methodsmentioning

confidence: 99%

Face Recognition Using MATLAB

Africa¹,

Abello²,

Gacuya³

et al. 2019

IJATCSE

View full text Add to dashboard Cite

This project dives into the topic of facial recognition and facial detection in a digital communications system. Face recognition is a technology that is widely used today which brings various benefits to society. Facial recognition differs from facial detection in the aspect that facial detection only finds and detects the present face/s in an image whereas, in facial recognition, the computer finds the face/s present in a subject and is able to distinguish the face from a sample of different faces. This research focuses on the implementation of both a facial recognition system and a facial detection system in MATLAB. This research would use the different imaging toolboxes available in the program and would be judged on its ability to accurately detect and recognize a sample in a given database. Additionally, this system should be able to create and to read a database of different faces.

show abstract

Section: Methodsmentioning

confidence: 99%

Face Recognition Using MATLAB

Africa¹,

Abello²,

Gacuya³

et al. 2019

IJATCSE

View full text Add to dashboard Cite

show abstract

“…Since the purpose of this study was to realize the recognition of mental health emotion of human expression fast by artificial intelligence, when collecting the facial expression image data of the volunteers, the corresponding mental health test was carried out, and the corresponding mental health labels were added for facial expression images. To ensure the time correspondence between the expression image and the degree of mental health in the database (i.e., the mental state reflected by the expression image was indeed the psychological state when the expression was collected), the expression data were collected by making the psychological evaluation of the volunteers to judge the mental health status and capturing the volunteers' expressions synchronously during the psychological evaluation [18]. Finally, 30 facial expression images were collected from each volunteer.…”

Section: Experimental Datamentioning

confidence: 99%

Research on Emotion Recognition Based on Deep Learning for Mental Health

Peng¹

2021

IJCAI

View full text Add to dashboard Cite

“…The creation of visual data is important in image processing. With the support of problem solving abilities and the application of other disciplines, it has encouraged further research to be carried out [5].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Based on Color Dissimilarity

Karma¹,

Putra

Sudarma

et al. 2022

IJCAI

View full text Add to dashboard Cite

This study aims to develop a segmentation technique that can be used to detect objects in an image. The concept used is to imitate the human concept of recognizing an object based on its color difference. A color is considered different if it has different R, G or B values. Humans can only distinguish two colors clearly if they both have a DeltaE value of at least 8. The difference in DeltaE values is obtained from differences in the values of R, G or B, either alone or in pairs. The application of this concept to the segmentation technique has shown good results.This technique is able to give the same results and is good for images with JPG, PNG and BMP file types. To provide confidence in the performance of this method, a comparison is made with several segmentation methods. By measuring the similarity index using the Jaccard index, Sørensen-Dice index and BF Score, this color dissimilarity-based segmentation method has the best performance. Meanwhile, when measuring the processing time, this method has the second best performance. Thus, it can be concluded that this method is very suitable for detecting objects in an image. Povzetek: Tehnika segmentacije je bila razvita z imitacijo človeškega koncepta prepoznavanja predmeta, in sicer na podlagi barvnih razlik. Barve se štejejo za različne, če imajo vrednost DeltaE najmanj 8, ki jo dobimo iz razlik v vrednostih R, G ali B barv samih ali v parih. Izvajanje te tehnike kaže enake rezultate za datoteke JPG, PNG in BMP in je v veliko pomoč pri postopku identifikacije predmeta.

show abstract

Feature Extraction Trends for Intelligent Facial Expression Recognition: A Survey

Cited by 7 publications

References 28 publications

Face Recognition Using MATLAB

Face Recognition Using MATLAB

Research on Emotion Recognition Based on Deep Learning for Mental Health

Image Segmentation Based on Color Dissimilarity

Contact Info

Product

Resources

About