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The project started with carrying out data acquisition experiments
on live video streams and preliminary evaluation on the data at the
end users' site. This has helped us in identifying realistic imaging
conditions and corresponding constraints under which a machine vision
system can operate outside the usual laboratory environment. For
learning a face model for real-time detection and tracking, publically
available face databases were utilised
[9, 10]. These were from Olivetti
Research Lab. UK; the USENIX Faces Archive, USA; the Universities of
Essex and Manchester, UK; Bern and Bochum, Germany; MIT, CMU, Harvard,
USA; and Weizmann Institute, Israel. The following parameters were
studied:
- the ratio of the size of a face to the overall
video-frame size, i.e. the accessible camera field of view with respect to
the acceptable resolution for recognition,
- the acceptable level of lighting variations under which the face
detection, tracking and normalisation processes of the system can be
performed consistently for both training and recognition tasks,
- the required size of databases for moving faces to be used for
learning temporal models for recognition in dynamic scenes,
- determining reference feature points on all faces to be
used for bootstrapping view alignment.
Further studies were carried out on identifying methods and
sources of variation in the acquisition of face database for both
learning a view-based face appearance model and applying such a model
for face recognition across views
[5, 7, 14]. More recently, a
novel approach for automatic acquisition of a face database across
views has been developed. Such a database has been used for learning
generic models for real-time head pose estimation and tracking
[21], and facial identity recognition and tracking across
views [8].