A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network

Thai, Le Hoang; Nguyen, Nguyen Do Thai; Hai, Tran Son

doi:10.7763/ijmlc.2011.v1.57

Cited by 30 publications

(14 citation statements)

References 41 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Local feature analysis in facial expression is very significant for facial expression analysis. Canny is used to detect local region such as: left-right eyes, mouth, leftright eyebrows [5]. In first phase, we use ICA to present local features in small presenting space.…”

Section: Local Feature Extractionmentioning

confidence: 99%

Facial Expression Classification Using Artificial Neural Network and K-Nearest Neighbor

Hai

Thai²,

Thuy³

2015

IJITCS

Self Cite

View full text Add to dashboard Cite

Facial Expression is a key component in evaluating a person's feelings, intentions and characteristics. Facial Expression is an important part of human-computer interaction and has the potential to play an equal important role in humancomputer interaction. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) applying for facial expression classification. We propose the ANN_KNN model using ANN and K-NN classifier. ICA is used to extract facial features. The ratios feature is the input of K-NN classifier. We apply ANN_KNN model for seven basic facial expression classifications (anger, fear, surprise, sad, happy, disgust and neutral) on JAFEE database. The classifying precision 92.38% has been showed the feasibility of our proposal model.

show abstract

Section: Local Feature Extractionmentioning

confidence: 99%

Facial Expression Classification Using Artificial Neural Network and K-Nearest Neighbor

Hai

Thai²,

Thuy³

2015

IJITCS

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Facial Expressionmentioning

confidence: 99%

“…In cases involving facial expressions, we used facial feature points from previous research efforts that employed artificial neural networks (ANNs) to conduct facial analysis (Karthigayan et al 2008;Thai et al 2011). Thai et al (Thai et al 2011) performed edge detection using the Canny algorithm to recognize facial expressions with an ANN. Thai et al (Thai et al 2011) did not extract feature points from all faces, but instead analyzed local features that might have a direct impact on facial expressions involving the eyes, mouth, brow, and other facial areas.…”

Section: Facial Expressionmentioning

confidence: 99%

“…Thai et al (Thai et al 2011) performed edge detection using the Canny algorithm to recognize facial expressions with an ANN. Thai et al (Thai et al 2011) did not extract feature points from all faces, but instead analyzed local features that might have a direct impact on facial expressions involving the eyes, mouth, brow, and other facial areas.…”

Section: Facial Expressionmentioning

confidence: 99%

See 1 more Smart Citation

Peer Review #2 of "Efficiently detecting outlying behavior in video-game players (v0.2)"

2015

View full text Add to dashboard Cite

In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects outlying behavior based on the characteristics of game players. These characteristics are captured noninvasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behavior. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

show abstract

“…The system developed achieved a recognition accuracy of 83%. The work presented in [16] recognizes facial emotions based on a novel approach using Canny, principal component analysis technique for local facial feature extraction and artificial neural network for the classification process. The average facial expression classification accuracy of the method is reported to be 85.7%.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Emotions from Facial Expressions Using Neural Network

Perikos

Ziakopoulos

Hatzilygeroudis

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Recognizing the emotional state of a human from his/her facial gestures is a very challenging task with wide ranging applications in everyday life. In this paper, we present an emotion detection system developed to automatically recognize basic emotional states from human facial expressions. The system initially analyzes the facial image, locates and measures distinctive human facial deformations such as eyes, eyebrows and mouth and extracts the proper features. Then, a multilayer neural network is used for the classification of the facial expression to the proper emotional states. The system was evaluated on images of human faces from the JAFFE database and the results gathered indicate quite satisfactory performance.

show abstract

A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network

Cited by 30 publications

References 41 publications

Facial Expression Classification Using Artificial Neural Network and K-Nearest Neighbor

Facial Expression Classification Using Artificial Neural Network and K-Nearest Neighbor

Peer Review #2 of "Efficiently detecting outlying behavior in video-game players (v0.2)"

Recognizing Emotions from Facial Expressions Using Neural Network

Contact Info

Product

Resources

About