A Database of Non-Manual Signs in Turkish Sign Language

Aran, Oya; Arı, İsmail; Güvensan, M. Amaç; Haberdar, Hakan; Kurr, Zeyneb; Türkmen, İrem; Uyar, Ash; Akarun, Lale

doi:10.1109/siu.2007.4298708

Cited by 28 publications

(13 citation statements)

References 4 publications

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“…Since this paper classifies the most common non-manual signs (head motion and facial expressions) in Turkish Sign Language (TSL) and Indian Sign Language (ISL), a video database of ISL is created and a database of TSL is taken from Aran et al [2].…”

Section: Resultsmentioning

confidence: 99%

Automatic Facial Expression Recognition in an Image Sequence of Non-Manual Indian Sign Language Using Support Vector Machine

Saraswat

Arya

2012

Advances in Intelligent and Soft Computing

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Section: Resultsmentioning

confidence: 99%

Automatic Facial Expression Recognition in an Image Sequence of Non-Manual Indian Sign Language Using Support Vector Machine

Saraswat

Arya

2012

Advances in Intelligent and Soft Computing

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“…The feature dimensionality is 104 per frame. More information on the database and feature extraction can be found in [29,30].…”

Section: Buhmap Turkish Non-manual Signals Databasementioning

confidence: 99%

A multi-class classification strategy for Fisher scores: Application to signer independent sign language recognition

Aran

Akarun

2010

Pattern Recognition

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Fisher kernels combine the powers of discriminative and generative classifiers by mapping the variable-length sequences to a new fixed length feature space, called the Fisher score space. The mapping is based on a single generative model and the classifier is intrinsically binary. We propose a strategy that applies a multiclass classification on each Fisher score space and combines the decisions of multiclass classifiers. We experimentally show that the Fisher scores of one class provide discriminative information for the other classes as well. We compare several multiclass classification strategies for Fisher scores generated from the Hidden Markov Models (HMMs) of sign sequences. The proposed multi-class classification strategy increases the classification accuracy in comparison with the state of the art strategies based on combining binary classifiers. To reduce the computational complexity of the Fisher score extraction and the training phases, we also propose a score space selection method and show that, similar or even higher accuracies can be obtained by using only a subset of the score spaces. Based on the proposed score space selection method, a signer adaptation technique is also presented that does not require any re-training.

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“…For recognizing these gestures, they tracked head position and rotation, then computed head velocity vector and used SVM classifiers. In Aran's study [26] a multi-class classification strategy for Fisher scores was proposed and tested on a hand gesture dataset and a sign language expression dataset [27].…”

Section: Related Workmentioning

confidence: 99%

“…Note that, we run the Adaboost-based feature selection separately for each experiment [27] Head shaking (G1):…”

Section: Classification With Svmmentioning

confidence: 99%

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Spatiotemporal-Boosted DCT Features for Head and Face Gesture Analysis

Akakın

Sankur

2010

Human Behavior Understanding

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Abstract. Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in humancomputer interfaces. In this study, facial landmark points are detected and tracked over successive video frames using a robust method based on subspace regularization, Kalman prediction and refinement. The trajectories (time series) of facial landmark positions during the course of the head gesture or facial expression are organized in a spatiotemporal matrix and discriminative features are extracted from the trajectory matrix. Alternatively, appearance based features are extracted from DCT coefficients of several face patches. Finally Adaboost algorithm is performed to learn a set of discriminating spatiotemporal DCT features for face and head gesture (FHG) classification. We report the classification results obtained by using the Support Vector Machines (SVM) on the outputs of the features learned by Adaboost. We achieve 94.04% subject independent classification performance over seven FHG.

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A Database of Non-Manual Signs in Turkish Sign Language

Cited by 28 publications

References 4 publications

Automatic Facial Expression Recognition in an Image Sequence of Non-Manual Indian Sign Language Using Support Vector Machine

Automatic Facial Expression Recognition in an Image Sequence of Non-Manual Indian Sign Language Using Support Vector Machine

A multi-class classification strategy for Fisher scores: Application to signer independent sign language recognition

Spatiotemporal-Boosted DCT Features for Head and Face Gesture Analysis

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