Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper ThAT9.7
A Vision-Based Taiwanese Sign Language Recognition System
Huang, Chung-Lin, National Tsing-Hua Univ.
Tsai, Bo-Lin, National Tsing-Hua Univ.
This paper presents a vision-based continuous sign language recognition system to interpret the Taiwanese Sign Language
(TSL). The continuous sign language, which consists of a sequence of hold and movement segments, can be decomposed
into non-signs and signs. The signs can be either static signs or dynamic signs. The former can be found in the hold
segment, whereas the latter can be identified in the combination of hold and movement segments. We use Support Vector
Machine (SVM) to recognize the static sign and apply HMM model to identify the dynamic signs. Finally, we use the
finite state machine to verify the correctness of the grammar of the recognized TSL sentence, and correct the miss-recognized
signs.
09:00-11:10, Paper ThAT9.8
Fusing Audio-Visual Nonverbal Cues to Detect Dominant People in Group Conversations
Aran, Oya, Idiap Res. Inst.
Gatica-Perez, Daniel,
This paper addresses the multimodal nature of social dominance and presents multimodal fusion techniques to combine
audio and visual nonverbal cues for dominance estimation in small group conversations. We combine the two modalities
both at the feature extraction level and at the classifier level via score and rank level fusion. The classification is done by
a simple rule-based estimator. We perform experiments on a new 10-hour dataset derived from the popular AMI meeting
corpus. We objectively evaluate the performance of each modality and each cue alone and in combination. Our results
show that the combination of audio and visual cues is necessary to achieve the best performance.
09:00-11:10, Paper ThAT9.9
Wavelet Domain Local Binary Pattern Features for Writer Identification
Du, Liang, Huazhong Univ. of Science and Tech.
You, Xinge, Huazhong Univ. of Science and Tech.
Xu, Huihui, Huazhong Univ. of Science and Tech.
Gao, Zhifan, Huazhong Univ. of Science and Tech.
Tang, Yuanyan, Hongkong Baptist University
The representation of writing styles is a crucial step of writer identification schemes. However, the large intra-writer variance
makes it a challenging task. Thus, a good feature of writing style plays a key role in writer identification. In this
paper, we present a simple and effective feature for off-line, text-independent writer identification, namely wavelet domain
local binary patterns (WD-LBP). Based on WD-LBP, a writer identification algorithm is developed. WD-LBP is able to
capture the essence of characteristics of writer while ignoring the variations intrinsic to every single writer. Unlike other
texture framework method, we do not assign any statistical distribution assumption to the proposed method. This prevent
us from making any, possibly erroneous, assumptions about the handwritten image feature distributions. The experimental
results show that the proposed writer identification method achieves high accuracy of identification and outperforms recent
writer identification method such as wavelet-GGD model and Gabor filtering method.
09:00-11:10, Paper ThAT9.10
Audio-Visual Classification and Fusion of Spontaneous Affective Data in Likelihood Space
Nicolaou, Mihalis, Imperial Coll.
Gunes, Hatice, Imperial Coll.
Pantic, Maja, Imperial Coll.
This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect,
utilising generative models for classification (i) in terms of Maximum Likelihood Classification with the assumption that
the generative model structure in the classifier is correct, and (ii) Likelihood Space Classification with the assumption
that the generative model structure in the classifier may be incorrect, and therefore, the classification performance can be
improved by projecting the results of generative classifiers onto likelihood space, and then using discriminative classifiers.
Experiments are conducted by utilising Hidden Markov Models for single cue classification, and 2 and 3-chain coupled
Hidden Markov Models for fusing multiple cues and modalities. For discriminative classification, we utilise Support
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