Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis

Ou, Jun; Bai, Xiaobo; Pei, Yun; Ma, Liang; Liu, Wei

doi:10.1109/iccms.2010.45

Cited by 63 publications

(17 citation statements)

References 9 publications

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“…Gabor wavelets have been applied to many feature extraction problems (Huang et al, 2010;Ou et al, 2010;Parvin et al, 2012;Spampinato et al, 2010) due to its salient visual properties such as spatial localization, frequency characteristics and orientation. Assume an image I given by I(x, y), the Gabor wavelet transform is the convolution between the function g and image I, given by equation.…”

Section: Gabor Wavelet Featuresmentioning

confidence: 99%

Automatic classification of flying bird species using computer vision techniques

Atanbori

Duan

Murray

et al. 2016

Pattern Recognition Letters

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Bird populations are identified as important biodiversity indicators, so collecting reliable population data is important to ecologists and scientists. However, existing manual monitoring methods are labour-intensive, time-consuming, and potentially error prone. The aim of our work is to develop a reliable automated system, capable of classifying the species of individual birds, during flight, using video data. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than while stationary. We present our work, which uses a new and rich set of appearance features for classification from video. We also introduce motion features including curvature and wing beat frequency. Combined with Normal Bayes classifier and a Support Vector Machine classifier, we present experimental evaluations of our appearance and motion features across a data set comprising 7 species. Using our appearance feature set alone we achieved a classification rate of 92% and 89% (using Normal Bayes and SVM classifiers respectively) which significantly outperforms a recent comparable state-of-the-art system. Using motion features alone we achieved a lower-classification rate, but motivate our on-going work which we seeks to combine these appearance and motion feature to achieve even more robust classification.

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Section: Gabor Wavelet Featuresmentioning

confidence: 99%

Automatic classification of flying bird species using computer vision techniques

Atanbori

Duan

Murray

et al. 2016

Pattern Recognition Letters

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“…Ou et al [30] defined 28 feature points proximal to facial components for measuring facial expression by using 40 Gabor filters comprising five frequency types in eight directions. Because of the high volume of the derived feature vectors, principal component analysis was used to reduce the data dimensions, and k-nearest neighbors was subsequently used to categorize the feature vectors into one of six expression types.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Effective semantic features for facial expressions recognition using SVM

Hsieh¹,

Hsih²,

Jiang³

et al. 2015

Multimed Tools Appl

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Most traditional facial expression-recognition systems track facial components such as eyes, eyebrows, and mouth for feature extraction. Though some of these features can provide clues for expression recognition, other finer changes of the facial muscles can also be deployed for classifying various facial expressions. This study locates facial components by active shape model to extract seven dynamic face regions (frown, nose wrinkle, two nasolabial folds, two eyebrows, and mouth). Proposed semantic facial features could then be acquired using directional gradient operators like Gabor filters and Laplacian of Gaussian. A multi-class support vector machine (SVM) was trained to classify six facial expressions (neutral, happiness, surprise, anger, disgust, and fear). The popular Cohn-Kanade database was tested and the average recognition rate reached 94.7 %. Also, 20 persons were invited for on-line test and the recognition rate was about 93 % in a real-world environment. It demonstrated that the proposed semantic facial features could effectively represent changes between facial expressions. The time complexity could be lower than the other SVM based approaches due to the less number of deployed features.

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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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Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis

Cited by 63 publications

References 9 publications

Automatic classification of flying bird species using computer vision techniques

Automatic classification of flying bird species using computer vision techniques

Effective semantic features for facial expressions recognition using SVM

Yüz ifadelerinin otomatik analizi üzerine bir literatür çalışması

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