Facial Expression Classification using Gabor and Log-Gabor Filters

Rose, Nectarios

doi:10.1109/fgr.2006.49

Cited by 47 publications

(25 citation statements)

References 11 publications

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“…This is the case of Gabor filters, which are robust to illumination changes and detect face edges on multiple scales and with different orientations [69,36,21,4,5,51], the Local Binary Patterns (LBP) [56] and Volumetric Local Binary Patterns [70] and also of the eigenface approaches [61].…”

Section: Related Workmentioning

confidence: 99%

Recognising facial expressions in video sequences

Buenaposada

Muñoz

Baumela

2007

Pattern Anal Applic

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We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89% recognition rate in a set of 333 sequences from the Cohn-Kanade data base.

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Section: Related Workmentioning

confidence: 99%

Recognising facial expressions in video sequences

Buenaposada

Muñoz

Baumela

2007

Pattern Anal Applic

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“…RBF neural network and fuzzy inference system is used for recognizing facial expressions. Zhengyou Zhang et al [23] presented a FER system where they have compared the use of two type of features extracted from face images for recognizing facial expression .Geometric positions of set of fiducial point and multiscale & multi orientation gobor wavelet coefficient extracted from the face image at the fiducial points are the two approaches used for feature extraction. These are given to neural network classifier separately or jointly and results were compared.…”

Section: Survey Of Existing Methodsmentioning

confidence: 99%

Automatic Facial Feature Extraction and Expression Recognition based on Neural Network

Khandait¹,

Thool²,

Khandait³

2011

IJACSA

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Abstract-In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.

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“…İlk aşamada yüz görüntülerindeki yüzün tamamından ya da belirli yüz bölgelerinden görünüm özellikleri çıkarılarak bir özellik vektörü oluşturulur [63]. Gabor filtresi [73][74][75][76] veya Yerel İkili Örüntü (LBP) operatörü [30,38,77,78] gibi teknikler yüz görünüm özelliklerini tespit ederek bir özellik vektörü oluşturmak için sıkça kullanılmaktadır. Daha sonra elde edilen özellik vektörü Destek Vektör Makinesi (SVM), Sinir Ağı (NN), Naive Bayesian (NB) gibi sınıflandırma yöntemlerine girdi olarak verilmektedir [79].…”

Section: Yüz İfadelerine Ait öZelliklerin çıKarılması (Extraction Of unclassified

Yüz ifadelerinin otomatik analizi üzerine bir literatür çalışması

Bayrakdar¹,

Akgün²,

Yücedağ³

2015

SAUFenBilDer

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ÖZYüz ifadeleri, duygulara kişilerarası iletişimde anlam bütünlüğü kazandıran evrensel işaretlerdir. Aynı zamanda yüz ifadelerindeki değişimler, duygu psikolojisinde en önemli ipuçları olarak kabul edilmektedir. Yüz ifade analizinin; insan davranışlarının analizi, insan-insan etkileşimi ve insan-bilgisayar etkileşimi gibi çok geniş bir uygulama yelpazesi vardır. İnsanlar yüz ifadelerini tanımada ve yorumlamada herhangi bir sorun yaşamazken bu işlem bilgisayarlarda zorlu bir problem olarak karşımıza çıkmaktadır. Fakat özellikle insan-bilgisayar etkileşiminde yaşanan gelişmelerle birlikte, insanlara ait duyguların bilgisayarlar tarafından anlaşılması elzem bir konu haline gelmiştir. Bunun yanında güvenlik, psikoloji, sağlık, robotik ve sanal gerçeklilik gibi birçok çalışma alanında da yüz ifadelerinin analizi ve tanıması giderek yaygınlaşmıştır. Bu nedenlerden dolayı, yüz ifadelerinin hızlı bir şekilde analiz edilmesi ve analiz edilen yüz ifadelerinin doğru olarak tanınması farklı uygulama alanlarında birçok yazılım sistemi için kritik bir rol oynamaktadır. Bu çalışmada, yüz ifadelerinin tanıması ile ilgili literatürde yapılan çalışmalar incelenmiş ve yüz ifadelerinin analizinde kullanılan bazı yöntemlere yer verilmiştir.Anahtar Kelimeler: yüz ifade analizi, yüz ifadesi tanıma, görüntü işleme A survey on automatic analysis of facial expressions ABSTRACT Facial expressions are universal symbols of emotions that provide cohesion to interpersonal communication. At the same time, the changes in facial expressions are considered to be the most important hints in the psychology of emotion. Facial expression analysis has widespread range of application in areas such as analysis of human behaviors, human-human interaction and human-computer interaction. While humans do not have any difficulty for recognizing and interpreting facial expressions, it is challenging problem for the computers. However, especially with advances in human-computer interaction, understanding the human emotions by computer has become an indispensable issue. Furthermore, analysis and recognition of facial expressions has pervaded in various areas such as security, psychology, health, robotics and virtual reality. For these reasons, the analyzing of facial expressions quickly and the recognition of facial expressions correctly according to the analyzed facial expressions play a critical role for many software systems in different applications. In this study, the methods used in the analysis of facial expressions and the studies in the literature on emotion recognition were investigated.

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Facial Expression Classification using Gabor and Log-Gabor Filters

Cited by 47 publications

References 11 publications

Recognising facial expressions in video sequences

Recognising facial expressions in video sequences

Automatic Facial Feature Extraction and Expression Recognition based on Neural Network

Yüz ifadelerinin otomatik analizi üzerine bir literatür çalışması

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