Automated face detection and recognition for a login system
| dc.contributor.advisor | Herbst, B. M. | |
| dc.contributor.advisor | Muller, N. | |
| dc.contributor.author | Louw, Lloyd A. B. | |
| dc.contributor.other | University of Stellenbosch. Faculty of Science. Dept. of Mathematical Sciences. Applied Mathematics. | |
| dc.date.accessioned | 2008-01-15T09:41:48Z | en_ZA |
| dc.date.accessioned | 2010-06-01T08:32:56Z | |
| dc.date.available | 2008-01-15T09:41:48Z | en_ZA |
| dc.date.available | 2010-06-01T08:32:56Z | |
| dc.date.issued | 2007-03 | |
| dc.description | Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2007. | |
| dc.description.abstract | The face is one of the most characteristic parts of the human body and has been used by people for personal identification for centuries. In this thesis an automatic process for frontal face recognition from 2–dimensional images is presented based on principal component analysis. The goal is to use these concepts in eventual face–recognizing login software. The first step is detecting faces in images that are allowed a certain degree of clutter. This is achieved by skin colour detection in the HSV colourspace. This process indicates the area of the image most likely corresponding to the face. Extracting the face is achieved by morphological processing of this area of the image. The face is then normalized by a transformation that uses the eye coordinates as input. Automatic eye detection is implemented based on colour analysis of the facial images and a 91.1% success rate is achieved. Recognition of the normalized faces is achieved using eigenfaces. To calculate these, a large enough database of facial images is needed. The xm2vts database is used in this thesis as the images have very constant lighting conditions throughout – an important factor affecting the accuracy of the recognition stage. Distinction is also made between identification and verification of faces. For identification, up to 80.1% accuracy is achieved, while for verification, the equal error rate is approximately 3.5%. | en |
| dc.format.extent | 7423095 bytes | en_ZA |
| dc.format.mimetype | application/pdf | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/1773 | |
| dc.language.iso | en | |
| dc.publisher | Stellenbosch : University of Stellenbosch | |
| dc.rights.holder | University of Stellenbosch | |
| dc.subject | Dissertations -- Applied mathematics | en |
| dc.subject | Theses -- Applied mathematics | en |
| dc.subject | Biometric identification | en |
| dc.subject | Computer security | en |
| dc.subject | Human face recognition (Computer science) | en |
| dc.title | Automated face detection and recognition for a login system | en |
| dc.type | Thesis |