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

y the reliability of component matching/alignment (i.e., matching scores for facial
components). Finally, the 2D semantic face graph of subject P can be learned from
N p images under similar pose by
G P = ⋃ i
F −1 {
1
N P
∑N P
k=1
SGD G P k
i
}
. (5.23)
The matching cost between the subject P and the k-th face image of subject Q can
be calculated as
C(P, Q k ) =
M∑
{
}
scw P (i) · scw Q k
(i) · SF D i (G P , G Qk ) , (5.24)
i=1
where M is the number of facial components.
Face matching is accomplished by
minimizing the matching cost.
5.4.2 Face Matching Algorithm
The system diagram of our proposed semantic face recognition method was illustrated
in Fig. 1.6 (in Chapter 1). Figure 5.15 describes the semantic face matching algorithm
for identifying faces with no rejection. The inputs of the algorithm are training
images of M known subjects in the enrollment phase and one query face image of an
unknown subject in the recognition phase. The query input can be easily generalized
to either multiple images of an unknown subject or multiple images of multiple unknown
subjects. Each known subject, P j , can have N j (≥ 1) training images. The
output of the algorithm is the identity of the unknown query face image(s) among M
known subjects (a rejection option can be included by providing a threshold on the
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