Facial Expression Analysis using Active Shape Model

Shbib, Reda; Zhou, Shikun

doi:10.14257/ijsip.2015.8.1.02

Cited by 25 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The one where AdaBoost classifier is a feature of the Support Vector Machine (SVM) method. [5] Data mining is a process that uses statistical techniques, calculations, artificial intelligence and machine learning to extract and identify useful information and related knowledge from large databases [5]. SVM initially can only classify data in two classes.…”

Section: Literature Reviewmentioning

confidence: 99%

On Identifying Facial Expression using Multiclass Ensemble Least-Squares Support Vector Machine

Sya'rani¹,

Lawi²,

Rayuwati³

et al. 2019

Proceedings of the Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January

View full text Add to dashboard Cite

Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person's mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Linear Discriminant Analysis (LDA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

On Identifying Facial Expression using Multiclass Ensemble Least-Squares Support Vector Machine

Sya'rani¹,

Lawi²,

Rayuwati³

et al. 2019

Proceedings of the Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January

View full text Add to dashboard Cite

show abstract

“…Yi et al [25] used feature points extracted by an active appearance model and extracted geometric and texture information based on the relative positions of those points. Shbib and Zhou [26] used features extracted on an active shape model and fed to a SVM to analyze the facial expressions in a one-against-one training-testing procedure.…”

Section: Hybrid Featuresmentioning

confidence: 99%

Late fusion of facial dynamics for automatic expression recognition

Bandrabur¹,

Florea²,

Florea³

et al. 2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Installment of a facial expression is associated with contractions and extensions of specific facial muscles.Noting that expression is about changes, we present a model for expression classification based on facial landmarks dynamics. Our model isolates the trajectory of facial fiducial points by wrapping them up in relevant features and discriminating among various alternatives with a machine learning classification system. The used features are geometric and temporal-based and the classification system is represented by a late fusion framework that combines several neural networks with binary responses. The proposed method is robust, being able to handle complex expression classes.

show abstract

“…Shibib developed a whole set of methods for facial expression recognition [13]. The facial features were extracted directly from ASM, and were not processed.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression Recognition Using Digitalised Facial Features Based on Active Shape Model

Sun¹,

Chen²,

Day³

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

Facial Expression Recognition is a hot topic in recent years. As artificial intelligent technology is growing rapidly, to communicate with machines, facial expression recognition is essential. The recent feature extraction methods for facial expression recognition are similar to face recognition, and those caused heavy load for calculation. In this paper, Digitalized Facial Features based on Active Shape Model method is used to reduce the computational complexity and extract the most useful information from the facial image. The result shows by using this method the computational complexity is dramatically reduced, and very good performance was obtained compared with other extraction methods.

show abstract

Facial Expression Analysis using Active Shape Model

Abstract: Facial expressions analysis is a vital part of the research in human-

Cited by 25 publications

References 18 publications

On Identifying Facial Expression using Multiclass Ensemble Least-Squares Support Vector Machine

On Identifying Facial Expression using Multiclass Ensemble Least-Squares Support Vector Machine

Late fusion of facial dynamics for automatic expression recognition

Facial Expression Recognition Using Digitalised Facial Features Based on Active Shape Model

Contact Info

Product

Resources

About