Comprehensive database for facial expression analysis

Kanade, Takeo; Cohn, Jeffrey F.; Tian, Ying-li

doi:10.1109/afgr.2000.840611

Cited by 1,887 publications

(1,168 citation statements)

References 16 publications

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“…Moreover, in our comparison we include RP [35] and also we directly feed the initial high dimensional samples without performing dimensionality reduction to a multiclass SVM classifier, to serve as our baseline testing methods. Experiments have been performed for facial expression recognition on the Cohn-Kanade database [36], for face recognition on "Labeled Faces in the Wild" (LFW) [37], Extended Yale B [38] and AR [39] datasets and for object recognition on ETH-80 image dataset [40].…”

Section: Resultsmentioning

confidence: 99%

Maximum Margin Projection Subspace Learning for Visual Data Analysis

Nikitidis

Tefas

Pitas

2014

IEEE Trans. on Image Process.

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Abstract-Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms.

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Section: Resultsmentioning

confidence: 99%

Maximum Margin Projection Subspace Learning for Visual Data Analysis

Nikitidis

Tefas

Pitas

2014

IEEE Trans. on Image Process.

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“…The experiments are conducted on the commonly used Cohn-Kanade facial expression database [8]. As some subjects in CK database show less than six facial expressions, we use a subset of thirty subjects with six basic facial expressions (surprise, sadness, fear, disgust, anger, and happiness).…”

Section: Resultsmentioning

confidence: 99%

Facial Expression Recognition via Sparse Representation

Zhi

Ruan

Wang

2012

IEICE Trans. Inf. & Syst.

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SUMMARYA facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of "important" training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images. key words: sparse representation, fuzzy integral, decision level fusion, facial expression recognition

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“…The Cohn-Kanade facial expression database [23] is the most widely used database in research on automated facial expression analysis. This database contains image sequences of approximately 100 subjects posing a set of 23 facial displays, and it contains FACS codes in addition to basic emotion labels.…”

Section: Commonly Used Databasesmentioning

confidence: 99%