Facial Expression Recognition Using Facial Movement Features

Zhang, Ligang; Tjondronegoro, Dian

doi:10.1109/t-affc.2011.13

Cited by 213 publications

(75 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is mainly because that we infer expressions from the corresponding AUs, and AU1, AU2, AU27 for surprise initialize all AU nodes with ground truth, and then infer the expression. We achieve an average expression recognition rate of 95.15% in this case, which is similar as the state of the art method in [32](95.1%) and [40](94.48%).…”

Section: ) Expression Recognitionsupporting

confidence: 65%

Simultaneous Facial Feature Tracking and Facial Expression Recognition

Wang

Zhao

et al. 2013

IEEE Trans. on Image Process.

147

View full text Add to dashboard Cite

The tracking and recognition of facial activities from images or videos attracted great attention in computer vision field. Facial activities are characterized by three levels: First, in the bottom level, facial feature points around each facial component, i.e., eyebrow, mouth, etc, capture the detailed face shape information; Second, in the middle level, facial action units (AUs), defined in Facial Action Coding System, represent the contraction of a specific set of facial muscles, i.e., lid tightener, eyebrow raiser, etc; Finally, in the top level, six prototypical facial expressions represent the global facial muscle movement and are commonly used to describe the human emotion state. In contrast to the mainstream approaches, which usually only focus on one or two levels of facial activities, and track (or recognize) them separately, this paper introduces a unified probabilistic framework based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent the facial evolvement in different levels, their interactions and their observations. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge. Given the model and the measurements of facial motions, all three levels of facial activities are simultaneously recognized through a probabilistic inference. Extensive experiments are performed to illustrate the feasibility and effectiveness of the proposed model on all three level facial activities.

show abstract

Section: ) Expression Recognitionsupporting

confidence: 65%

Simultaneous Facial Feature Tracking and Facial Expression Recognition

Wang

Zhao

et al. 2013

IEEE Trans. on Image Process.

147

View full text Add to dashboard Cite

show abstract

“…Hence, Kowalik 22 made use of a set of points located around the mouth in order to train a neural classifier. In 2011, Zhang 40 proposed a Facial Expression Recognition (FER) system by using "salient" distance features in order to consider facial element and muscle movements. More recently, Shan 29 introduced an efficient approach to smile detection, in which the intensity differences between pixels in the grayscale face images are used as features.…”

Section: State Of the Artmentioning

confidence: 99%

An Evolutive Approach for Smile Recognition in Video Sequences

Freire-Obregón

Castrillón-Santana

2015

Int. J. Patt. Recogn. Artif. Intell.

View full text Add to dashboard Cite

Facial expression recognition is one of the most challenging research areas in the image recognition field and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human-machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future. We are talking about quantifying the emotion at low computation cost and high accuracy. For this aim, we have used a new support vector machine (SVM) based approach that integrates a weighted combination of local binary patterns (LBPs) and principal component analysis (PCA) based approaches. Furthermore, we construct this smile detector considering the evolution of the emotion along its natural life cycle. As a consequence, we achieved both low computation cost and high performance with video sequences.

show abstract

“…Mandeep Kaur, Rajeev Vashisht and Jalandhar Nirvair Neeru in year 2010 [14] proposed a system based on Principal Component Analysis and Singular Value Decomposition. The experiment was tested on JAFEE and accuracy was of each expression was calculated separately.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This 1-to-N comparison requires establishing the identity of a person's expression with no other information other than that contained within the live template itself. The challenge with facial recognition is the facial expression [14]. The proposed algorithm will provide effective accuracy for recognition of the facial expression of an individual.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Human Facial Expression Recognition using Gabor Filter and Eigenvector

Dhankhar¹,

Sahu²

2014

IJCA

View full text Add to dashboard Cite

Facial Expression Recognition has always been topic of interest for research in field of human computer/machine interaction based on machine learning and image processing tools. However, the inaccuracy of such systems is always challenging. Hence to resolve this problem, a novel algorithm using Gabor Filter and Eigenvector Based Distributed Feature, is proposed in this paper. The work is enhancement of existing Face Identification by covering the challenges of Facial Expression. The approach combines face recognition along with facial expression recognition. Over the time authentication system is replaced by biometric authentication. These systems are based on some human features and behavior. The mean accuracy is improved to 93.4%. The effectiveness and stability is shown in experimental results of the algorithm.

show abstract

Facial Expression Recognition Using Facial Movement Features

Cited by 213 publications

References 46 publications

Simultaneous Facial Feature Tracking and Facial Expression Recognition

Simultaneous Facial Feature Tracking and Facial Expression Recognition

An Evolutive Approach for Smile Recognition in Video Sequences

A Novel Algorithm for Human Facial Expression Recognition using Gabor Filter and Eigenvector

Contact Info

Product

Resources

About