Journal
PATTERN RECOGNITION LETTERS
Volume 34, Issue 16, Pages 2051-2056Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2013.06.022
Keywords
Sign language recognition; Conditional random field; BoostMap embedding; Support vector machine
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Funding
- World Class University Program through the National Research Foundation of Korea
- Ministry of Education, Science, and Technology [R31-10008]
- National Research Foundation of Korea (NRF)
- Korea government (MEST) [2009-0086841]
- National Research Foundation of Korea [2009-0086841] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The sign language is composed of two categories of signals: manual signals such as signs and fingerspellings and non-manual ones such as body gestures and facial expressions. This paper proposes a new method for recognizing manual signals and facial expressions as non-manual signals. The proposed method involves the following three steps: First, a hierarchical conditional random field is used to detect candidate segments of manual signals. Second, the BoostMap embedding method is used to verify hand shapes of segmented signs and to recognize fingerspellings. Finally, the support vector machine is used to recognize facial expressions as non-manual signals. This final step is taken when there is some ambiguity in the previous two steps. The experimental results indicate that the proposed method can accurately recognize the sign language at an 84% rate based on utterance data. (C) 2013 Elsevier B. V. All rights reserved.
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