Fusion of Visual and Thermal Face Recognition Techniques: A ...

In face recognition, the shortest distance between the gallery and subject can be
measured and the first matched for verification or the first 10 or 100 can be matched the
identification. The Euclidean distance, D , is used to compared two feature sets,
where N in the number of the features and Pi
and
∑ N 2
D = || Pi - Qi
|| , (6)
i= 1
Q
i
is the matrix of the feature sets.
Since the features of the faces have different degrees of significance and reliability
for representing a face, the weighted Euclidean distance, Dw , should be used to compare
two feature sets. If we choose different feature sets, they will contribute differently in the
recognition process. The main problem is how to find each weight.
Dw =
N
∑
i=
1
where ki
is the weighting factors for Pi
and Q i .
2
k || P − Q || , (7)
i
i
Figure 24 shows the performance of different sets of landmark points and
geometrical distances, while Figure 25 shows FaceIt® outperform our experimental
results. This indicates that the features what we extract is not a good features for face
recognition than FaceIt® has. A plot of probabilities of correct match versus the number
of best similarity scores called a cumulative match characteristic curve (CMC) is mainly
used in this paper for the evaluation of performance of face identification. Another
performance method called a receiver characteristic curve (ROC), a plot of numerous
false acceptance rate and false rejection rate combinations, which often is used for the
evaluation of face verification, will be included in the future.
i
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