Robust sign language recognition by combining manual and non-manual features based on conditional random field and support vector machine

• Combine manual and non-manual signals to recognize sign language.
• Apply a hierarchical CRF to discriminate between signs and fingerspelling.
• Recognize a facial expression to analyze the specific lexical meaning of signed utterance.

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.