Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings

Hee Deok Yang, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

A sign language consists of two types of action; signs and fingerspellings. Signs are dynamic gestures discriminated by continuous hand motions and hand configurations, while fingerspellings are a combination of continuous hand configurations. Sign language spotting is the task of detection and recognition of signs and fingerspellings in a signed utterance. The internal structures of signs and fingerspellings differ significantly. Therefore, it is difficult to spot signs and fingerspellings simultaneously. In this paper, a novel method for spotting signs and fingerspellings is proposed. It can distinguish signs, fingerspellings and non-sign patterns, and is robust to the various sizes, scales and rotations of the signer's hand. This is achieved through a hierarchical framework consisting of three steps: (1) Candidate segments of signs and fingerspellings are discriminated using a two-layer conditional random field (CRF). (2) Hand shapes of segmented signs and fingerspellings are verified using BoostMap embeddings. (3) The motions of fingerspellings are verified in order to distinguish those which have similar hand shapes and different hand motions. Experiments demonstrate that the proposed method can spot signs and fingerspellings from utterance data at rates of 83% and 78%, respectively.

Original languageEnglish
Pages (from-to)2858-2870
Number of pages13
JournalPattern Recognition
Volume43
Issue number8
DOIs
Publication statusPublished - 2010 Aug

Keywords

  • Conditional random field
  • Fingerspelling spotting
  • Sign language spotting

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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