This thesis relates to the design, implementation and evaluation of statistical
face recognition techniques. In particular, the use of Hidden Markov
Models in various forms is investigated as a recognition tool and critically
evaluated. Current face recognition techniques are very dependent on issues
like background noise, lighting and position of key features (ie. the eyes,
lips etc.). Using an approach which specifically uses an embedded Hidden
Markov Model along with spectral domain feature extraction techniques,
shows that these dependencies may be lessened while high recognition rates
are maintained.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2577 |
| Date | 03 1900 |
| Creators | Ballot, Johan Stephen Simeon |
| Contributors | Du Preez, J. A., Herbst, B. M., University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of Stellenbosch |
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