(a) (b)
Figure 7: Recognition rate combination plotted versus λ(a):
Recognition rate for σ = 12 and α = 115 (b): Recognition
rate for σ = 4 and α = 25.
4 EXPERIMENTAL RESULTS
Each subject was scanned under six different facial
expressions. Simultaneously a dataset of 2D color
(texture) images was also collected for use in Gabor
ﬁlter based recognition described later. In this section
we present some experimental results to demonstrate
effectiveness of our approach. As shown in the paper
(Samir et al., 2006), the geometric mean d
g
has given
the best recognition rate results. A combination
of shape and texture metrics provides a method to
compare textured, facial surfaces is proposed. Indeed,
in order to increase the accuracy of face recognition,
it is often necessary to integrate the results obtained
from different features of face: texture and shape.
Let d
t
be the distance between two faces based
on texture and d
g
be the distance between two faces
based on shape. Indeed, One of the difﬁculties
involved in integrating different distance measures
is the difference in the range of associated distances
values. In order to have an efﬁcient and robust
integration scheme, we normalize the two distances
values to be within the same range of [0,1]. The
normalization is done as follows:
d
tn
=
d
t
−d
t
min
d
t
max
−d
t
min
and d
gn
=
d
g
−d
g
min
d
g
max−d
g
min
We deﬁne an integrated distance d between two faces
as:
d = λd
tn
+ (1−λ)d
gn
0 ≤ λ ≤ 1 (3)
The main problem is the choice of the value of λ.
This idea is illustrated in Figure 7, where the recog-
nition performance is plotted against λ. The results
obtained in Figure 7 show that λ = 0.17 gives the best
recognition rate 98.9.
5 SUMMARY
A new metric on shapes of facial surfaces was pro-
posed in (Samir et al., 2006). In this paper, this
method is extended to include analysis of facial tex-
tures in the recognition process. We use a spectral
decomposition approach to analyze and compare 2D
faces, the choice of ﬁlters in this approach is impor-
tant and has a major bearing on the recognition perfor-
mance. The experimental results clearly show that the
combination of the texture information and the sur-
face features of the same person outperform methods
using one or other descriptor. For instance our method
achieved a recognition rate of 98.9% in the case of
recognizing faces under different facial expressions.
ACKNOWLEDGEMENTS
This work is supported by CNRS and GET under the
project Recovis3D.
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