Appearance-Based recognition of words in american sign language

Authors:
Morteza Zahedi;Daniel Keysers;Hermann Ney
Affiliations:
Lehrstuhl für Informatik VI – Computer Science Department, RWTH Aachen University, Aachen, Germany;Lehrstuhl für Informatik VI – Computer Science Department, RWTH Aachen University, Aachen, Germany;Lehrstuhl für Informatik VI – Computer Science Department, RWTH Aachen University, Aachen, Germany
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Year:
2005

Citing 4
Cited 7

Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
A System for Person-Independent Hand Posture Recognition against Complex Backgrounds

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation

ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Virtual 3-D interface system via hand motion recognition from two cameras

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Sign Language Recognition: Working with Limited Corpora

UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services
Large lexicon detection of sign language

HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Pronunciation clustering and modeling of variability for appearance-based sign language recognition

GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Combination of tangent distance and an image distortion model for appearance-based sign language recognition

PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Non parametric, self organizing, scalable modeling of spatiotemporal inputs: The sign language paradigm

Neural Networks
Non-manual cues in automatic sign language recognition

Personal and Ubiquitous Computing
Redundancy removal for isolated gesture in Indian sign language and recognition using multi-class support vector machine

International Journal of Computational Vision and Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present how appearance-based features can be used for the recognition of words in American sign language (ASL) from a video stream. The features are extracted without any segmentation or tracking of the hands or head of the signer, which avoids possible errors in the segmentation step. Experiments are performed on a database that consists of 10 words in ASL with 110 utterances in total. These data are extracted from a publicly available collection of videos and can therefore be used by other research groups. The video streams of two stationary cameras are used for classification, but we observe that one camera alone already leads to sufficient accuracy. Hidden Markov Models and the leaving one out method are employed for training and classification. Using the simple appearance-based features, we achieve an error rate of 7%. About half of the remaining errors are due to words that are visually different from all other utterances.