ISSN : 1796-2048
Volume : 1    Issue : 7    Date : November/December 2006

Learning to Recognize Faces by Successive Meetings
M. Castrillón-Santana, O. Déniz-Suárez, J. Lorenzo-Navarro, and M. Hernández-Tejera
Page(s): 1-8
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In this paper we focus on the face recognition problem. However, instead of following the usual
approach of manually gathering and registering face images to build a training set to compute a
classifier off-line, the system  will start with an empty training set, i.e. no experience, and it will build
it autonomously by continuous on-line learning. In that way the classifier evolves with the perceptual
experience of the system, similarly to the way humans do. Experiments have been performed with
310 sequences corresponding to 80 identities. Two different configurations have been analyzed
depending on the ability to detect new, i.e. unknown, identities. The results achieved evidence that if
a verification stage is included the system learns fast to detect new identities. For revisitors, the
accumulated error rate decreases in both cases, reaching around 50% if no verification is included.
These results seem to indicate that more interaction or meetings with the different individuals are
needed to affirm that their identity is familiar enough to be recognized robustly.

Index Terms
face recognition, face detection, exemplars selection, learning systems, online learning, support
vector machines, incremental PCA