No / In this paper we present a new method for accurate real-time facial feature detection. Our method is based on local feature detection and enhancement. Previous work in this area, such as that of Viola and Jones, require looking at the face as a whole. Consequently, such approaches have increased chances of reporting negative hits. Furthermore, such algorithms require greater processing power and hence they are especially not attractive for real-time applications. Through our recent work, we have devised a method to identify the face from real-time images and divide it into regions of interest (ROI). Firstly, based on a face detection algorithm, we identify the face and divide it into four main regions. Then, we undertake a local search within those ROI, looking for specific facial features. This enables us to locate the desired facial features more efficiently and accurately. We have tested our approach using the Cohn-Kanade’s Extended Facial Expression (CK+) database. The results show that applying the ROI has a relatively low false positive rate as well as provides a marked gain in the overall computational efficiency. In particular, we show that our method has a 4-fold increase in accuracy when compared to existing algorithms for facial feature detection.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/9482 |
| Date | January 2016 |
| Creators | Al-dahoud, Ahmad, Ugail, Hassan |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Book chapter, No full-text in the repository |
Page generated in 0.0015 seconds