Hand Tracking and Affine Shape-Appearance Handshape Sub-units in Continuous Sign Language Recognition

Roussos, Anastasios; Theodorakis, Stavros; Pitsikalis, Vassilis; Maragos, Petros

doi:10.1007/978-3-642-35749-7_20

Cited by 20 publications

(27 citation statements)

References 14 publications

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“…The similarity parameters are re-initialized per frame, allowing failure recovery and preventing error accumulation. In this work, we do not address fitting on occlusions, by first applying an algorithm for occlusion detection [54].…”

Section: Figure 2 Gaam Fitting Initialization Gaam Fitting Initializmentioning

confidence: 99%

Classification of extreme facial events in sign language videos

2014

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We propose a new approach for Extreme States Classification (ESC) on feature spaces of facial cues in sign language (SL) videos. The method is built upon Active Appearance Model (AAM) face tracking and feature extraction of global and local AAMs. ESC is applied on various facial cues -as, for instance, pose rotations, head movements and eye blinking -leading to the detection of extreme states such as left/right, up/down and open/closed. Given the importance of such facial events in SL analysis, we apply ESC to detect visual events on SL videos, including both American (ASL) and Greek (GSL) corpora, yielding promising qualitative and quantitative results. Further, we show the potential of ESC for assistive annotation tools and demonstrate a link of the detections with indicative higher-level linguistic events. Given the lack of facial annotated data and the fact that manual annotations are highly time-consuming, ESC results indicate that the framework can have significant impact on SL processing and analysis.

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Section: Figure 2 Gaam Fitting Initialization Gaam Fitting Initializmentioning

confidence: 99%

Classification of extreme facial events in sign language videos

2014

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“…The use of a decision tree improved scalability over previous individual classifier approaches but results in the entire tree needing to be rebuilt should a new template need to be incorporated. Roussos et al [86] employ an Affine-invariant Modelling of hand Shape-Appearance images, offering a compact and descriptive representation of the hand configuration. The hand shape features extracted via the fitting of this model are used to construct an unsupervised set of sub-units.…”

Section: Hand Shapementioning

confidence: 99%

Sign Language Recognition

Cooper

Holt

Bowden

2011

Visual Analysis of Humans

176

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This chapter covers the key aspects of Sign Language Recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.

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“…This large lexicon gives a wide range of signs, some contain similar motions and occasionally some signs share component parts. Hand and head trajectories are extracted from the videos using the work of Roussos et al [10]. Three types of features are extracted: motion of the hands; location of the sign being performed; and handshape used.…”

Section: Database Ii: Gsl 982 Signsmentioning

confidence: 99%

Sign Language Recognition using Sequential Pattern Trees

Ong

Cooper

Pugeault

et al. 2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect TM data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.

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Hand Tracking and Affine Shape-Appearance Handshape Sub-units in Continuous Sign Language Recognition

Cited by 20 publications

References 14 publications

Classification of extreme facial events in sign language videos

Classification of extreme facial events in sign language videos

Sign Language Recognition

Sign Language Recognition using Sequential Pattern Trees

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