Abstract book (pdf) - ICPR 2010

curacy for imagined sign. Pairwise comparison of phrases composed of these signs yielded a mean accuracy of 73.4%.
These results suggest the possibility of BCIs based on sign language.
09:00-11:10, Paper ThAT9.50
American Sign Language Phrase Verification in an Educational Game for Deaf Children
Zafrulla, Zahoor, Georgia Inst. of Tech.
Brashear, Helene, Georgia Inst. of Tech.
Yin, Pei, Georgia Inst. of Tech.
Presti, Peter, Georgia Inst. of Tech.
Starner, Thad, Georgia Inst. of Tech.
Hamilton, Harley, Georgia Inst. of Tech.
We perform real-time American Sign Language (ASL) phrase verification for an educational game, CopyCat, which is
designed to improve deaf children’s signing skills. Taking advantage of context information in the game we verify a phrase,
using Hidden Markov Models (HMMs), by applying a rejection threshold on the probability of the observed sequence for
each sign in the phrase. We tested this approach using 1204 signed phrase samples from 11 deaf children playing the game
during the phase two deployment of CopyCat. The CopyCat data set is particularly challenging because sign samples are
collected during live game play and contain many variations in signing and disfluencies. We achieved a phrase verification
accuracy of 83% compared to 90% real-time performance by a sign linguist. We report on the techniques required to reach
this level of performance.
09:00-11:10, Paper ThAT9.51
A Robust Method for Hand Gesture Segmentation and Recognition using Forward Spotting Scheme in Conditional
Random Fields
Elmezain, Mahmoud, Otto-von-Guericke-Univ. Magdeburg
Al-Hamadi, Ayoub, Otto-von-Guericke-Univ. Magdeburg
Michaelis, Bernd, Otto-von-Guericke-Univ. Magdeburg
This paper proposes a forward spotting method that handles hand gesture segmentation and recognition simultaneously
without time delay. To spot meaningful gestures of numbers (0-9) accurately, a stochastic method for designing a nongesture
model using Conditional Random Fields (CRFs) is proposed without training data. The non-gesture model provides
a confidence measures that are used as an adaptive threshold to find the start and the end point of meaningful gestures.
Experimental results show that the proposed method can successfully recognize isolated gestures with 96.51% and meaningful
gestures with 90.49% reliability.
09:00-11:10, Paper ThAT9.52
Real-Time Upper-Limbs Posture Recognition based on Particle Filters and AdaBoost Algorithms
Fahn, Chin-Shyurng, National Taiwan Univ. of Science and Tech.
Chiang, Sheng-Lung, National Taiwan Univ. of Science and Tech.
In this paper, we employ particle filters to dynamically locate a face and upper-limbs. To prevent from the disturbance
caused by skin color regions, such as other naked parts of a human body, or some skin color-like objects in the background,
we further take the motion cue as a feature during the tracking. Currently, we prescribe eight kinds of upper-limbs postures
with reference to the characteristic of flag semaphore. The advantage is that we can utilize the relative positions of a face
and two hands to recognize the postures easily. To achieve posture recognition, we evaluate three different classifiers using
the machine learning methods: multi-layer perceptrons, support vector machines, and AdaBoost algorithms. The experimental
results reveal that AdaBoost algorithms are the best one, which reach the accuracy rate of recognizing upper-limbs
postures more than 95% and require much less training time than the other two do.
09:00-11:10, Paper ThAT9.53
One-Lead ECG-Based Personal Identification using Ziv-Merhav Cross Parsing
Pereira Coutinho, David, Inst. Superior de Engenharia de Lisboa
Fred, Ana Luisa Nobre, Inst. Superior Técnico
Figueiredo, Mario A. T., Inst. Superior Técnico
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