초록

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Abstract ▼ AI-Helper

The sign language consists of two types of signals: manual signals (MSs) such as signs and fingerspellings and non-manual signals (NMSs) such as facial expressions and body gestures. This paper proposes a new method for recognizing manual signals and facial expressions as non-manual signals. The pro...

The sign language consists of two types of signals: manual signals (MSs) such as signs and fingerspellings and non-manual signals (NMSs) such as facial expressions and body gestures. 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.

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