Face Detection and Modeling for Recognition - Biometrics Research ...

low-level features can provide meaningful high-level semantics for human consumers.
There has been some work done on automatically classifying images into semantic
categories [154], such as indoors/outdoors and city/landscape images.
As for the
semantic concept of faces, the generic facial topology (e.g., our proposed generic semantic
face graph) is a useful structure for representing the face in a search engine.
We have designed a graphical user interface for face editing using our face detection
algorithm. Combined with our semantic face matching algorithm, we can build a face
retrieval system.
2.5 Summary
We have briefly described the development of face detection, face recognition, face
modeling and model compression in this chapter.
We have summarized the performance
of currently available face detection systems in Table 2.3. Note that the
performance of a detection system depends on several factors such as face databases
on which the system is evaluated, system architecture, distance metric, and algorithmic
parameters. The performance is evaluated based on the detection rate, the false
positive rate (false acceptance rate), and databases. In Table 2.3, we do not include
the false acceptance rate because the false positive rate has not been completely reported
in literature. We refer the reader to the FERET evaluation [68], [28] for the
performance of various face recognition systems.
Face detection and face recognition are closely related to each other in the sense
of categorizing faces. Over the past ten years, based on the statistical pattern theory,
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