Name
Abstract | ||
|---|---|---|
This article presents a secure method for identification of voice-less commands using mouth images, without evaluating sound signals. The main limitation in voice recognition technologies for internet applications is that the commands will be audible to other people in the vicinity. The proposed technique identifies the unspoken utterances using support vector machines. The proposed system is based on temporal integration of the video data to generate spatiotemporal templates (STT). Sixty-four Zernike Moments are extracted from each STT. The experimental results demonstrate that the proposed system yields promising in recognising English phonemes. The proposed technique is demonstrated to be invariant to global variations of illumination level. Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords and internet applications on mobile devices. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1504/IJESDF.2008.021450 | IJESDF |
Keywords | Field | DocType |
human-computer interaction, motion segmentation, support vector machines, visual speech recognition, ZM, Zernike Moments | Computer science,Support vector machine,Zernike polynomials,Speech recognition,Vision based,Mobile device,Password,Invariant (mathematics),The Internet | Journal |
Volume | Issue | ISSN |
1 | 4 | 1751-911X |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wai Chee Yau | 1 | 40 | 4.87 |
| Dinesh Kant Kumar | 2 | 168 | 28.34 |
| Hans Weghorn | 3 | 203 | 56.24 |