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Cascading multiple LDA classifiers for facial recognition

Dijk, M.N. van (2020) Cascading multiple LDA classifiers for facial recognition.

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Abstract:A classical approach to face recognition uses dimensionality reduction techniques to describe faces. A face can be projected onto a feature space that spans the significant variations among known face images. To design a classifier, a normal Gaussian distribution within this feature space is assumed. However, it was found assuming this kind of distribution is not optimal for facial recognition. In this thesis research will be done on subspace learning to deal with the small amount of data which lies outside the Gaussian distribution, but still has to be recognized by a classifier. To do this, a new way of face classification is proposed using classical facial recognition methods to cascade multiple classifiers. By cascading multiple classifiers, subsets initially not recognized by the first classifier can be classified. This thesis will investigate whether cascading multiple LDA classifiers can be beneficial for facial recognition and what the effect of several parameters is on the performance of this classification system. This is done by looking at both Authentics-Imposter Distribution curves, as well as ROC curves. Because the results show no improvements, it was concluded the sample clusters outside of the assumed distribution have to be modeled in a more accurate way.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Programme:Electrical Engineering BSc (56953)
Link to this item:http://purl.utwente.nl/essays/82357
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