Semi-supervised PCA-based face recognition using self-training

TitleSemi-supervised PCA-based face recognition using self-training
Publication TypeConference Paper
Year of Publication2006
AuthorsRoli, F, Marcialis, GL
EditorYeung, D, Kwok, J, Fred, A, Roli, F, de Ridder, D
Conference NameJoint IAPR Int. Workshop. on Structural and Syntactical Pattern Recognition and Statistical Techniques in Pattern Recognition S+SSPR06
Date Published17/08/2006
Conference LocationHong Kong (China)
Keywordsbio04, biometrics, template, update

Performances of face recognition systems based on principal component analysis can degrade quickly when input images exhibit substantial variations, due for example to changes in illumination or pose, compared to the templates collected during the enrolment stage. On the other hand, a lot of new unlabelled face images, which could be potentially used to update the templates and re-train the system, are made available during the system operation. In this paper a semi-supervised version, based on the self-training method, of the classical PCA-based face recognition algorithm is proposed to exploit unlabelled data for off-line updating of the eigenspace and the templates. Reported results show that the exploitation of unlabelled data by self-training can substantially improve the performances achieved with a small set of labelled training examples.

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