Face Recognition under Real-world Conditions
Ladislav Lenc and Pavel Kr´al
Department of Computer Science and Engineering, University of West Bohemia, Plzeˇn, Czech Republic
Automatic Face Recognition, Czech News Agency, Confidence Measures, Gabor Wavelets, Scale Invariant
Feature Transform.
This paper deals with Automatic Face Recognition (AFR). The main contribution of this work consists in
the evaluation of our two previously proposed AFR methods in real conditions. At rst, we compare and
evaluate the recognition accuracy of two AFR methods on well-controlled face database. Then we compare
these results with the recognition accuracy on a real-world database of comparable size. For such comparison,
we use a sub-set of the newly created Czech News Agency (
CTK) database. This database is created from
the real photos acquired by the
CTK and the creation of this corpus represents the second contribution of this
work. The experiments show the significant differences in the results on the controlled and real-world data.
100% accuracy is achieved on the ORL database while only 72.7% is the best score for the
CTK database.
Further experiments show, how the recognition rate is influenced by the number of training images for each
person and by the size of the database. We also demonstrate, that the recognition rate decreases significantly
with larger database. We propose a confidence measure technique as a solution to identify and to filter-out the
incorrectly recognized faces. We further show that confidence measure is very beneficial for AFR under real
Nowadays, Automatic Face Recognition (AFR) has
been one of the most progressive biometric methods.
The spectrum of the possible applications of the AFR
is really broad. It reaches from the surveillance of
people (police), access control to restricted areas to
the labeling of the photos used in the recently very
popular photo sharing applications or in the social
Many algorithms for the face recognition were
proposed. Most of them perform well under cer-
tain “good” conditions (face images are well aligned,
the same face pose and lighting conditions, etc.).
However, their performance is significantly degraded
when these conditions are not accomplished. Many
methods have been introduced to handle these limi-
tations, but only few of them perform satisfactorily
in a fully uncontrolled environment. The main goal
of this paper is thus to evaluate two previously pro-
posed methods (Lenc and Kr´al, 2012a; Lenc and Kr´al,
2012b) under real conditions. The evaluation will be
performed on a huge database owned by the Czech
News Agency (
To improve the results of the AFR methods, using
the confidence measures should be beneficial. There-
fore, we further use the previously proposed confi-
dence measure approach (Lenc and Kr´al, 2011) in the
post-processing step. This way we can classify cer-
tain amount of faces with high recognition accuracy
while the rest of faces remains unclassified.
The results of this work will be used by the
CTK in
order to label the unlabeled photos in the large photo-
database (about 2 millions pictures). Note that only
few labeled images of every person are available. Un-
fortunately, the labeled images don’t contain only the
head/face of the person, but also some other addi-
tional useless information (other people, background
objects, etc.). Therefore, another goal of this work
consists in the proposition of the algorithm for the
corpus creation.
The following section gives an overview of some
important AFR methods. Section 3 describes in more
detail the two AFR methods we used in this work;
namely the adapted Kepenekci method and the SIFT
based Kepenekci approach. Confidence measures are
also described in this section. Section 4 first intro-
duces the corpora used for testing. Then, the algo-
rithm for the corpus creation is presented. Further,
there is the description of the experiments performed
to evaluate the methods. In the last section we dis-
cuss the achieved results and give the direction of the
Lenc L. and Král P..
Face Recognition under Real-world Conditions.
DOI: 10.5220/0004237402500256
In Proceedings of the 5th International Conference on Agents and Artificial Intelligence (ICAART-2013), pages 250-256
ISBN: 978-989-8565-39-6
2013 SCITEPRESS (Science and Technology Publications, Lda.)
further research.
One of the first successful approaches is the Prin-
cipal Component Analysis (PCA), so called Eigen-
faces (Turk and Pentland, 1991). It is a statistical
method that takes into account the whole image as
a vector. Eigenvectors of the image matrix are used
for face representation. The method is sensitive on
variations in lighting conditions, pose and scale.
Another method, Fisherfaces, is derived from the
Fisher’s Linear Discriminant (FLD). A description of
this method is presented in (Belhumeur et al., 1997).
Similarly to the Eigenfaces, the Fisherfaces project an
image into another, less dimensional, space. Accord-
ing to the authors, this approach should be insensitive
to changing lighting conditions.
Another group of approaches use Neural Net-
works (NNs). Several NNs topologies were proposed.
One of the best performing methods based on neural
networks is presented in (Lawrence et al., 1997).
Hidden Markov Models (HMMs) are also suc-
cessfully used for AFR (Samaria and Young, 1994).
The method was tested on a dataset containing 5 im-
ages of each of the 24 individuals. Indicated recogni-
tion rate of this approach is 84%. For comparison, the
Eigenfaces were tested using the same dataset and the
recognition rate of 74% is reported.
In (Nefian and Hayes, 1998) another HMM-based
approach is described. It is stated there, that the
method significantly reduces the computational com-
plexity in comparison with the older methods while
the recognition rate remains the same.
In the last couple of years, several successful
approaches based on Gabor wavelets were intro-
duced (Shen and Bai, 2006). One of the first such
methods was proposed by Lades (Lades et al., 1993).
Some approaches (Shen, 2005) also combine the pre-
processing with Gabor wavelets with well established
methods such as Eigenfaces, Fisherfaces, etc. An-
other successful approach described in (Wiskott et al.,
1999; Bolme, 2003) is Elastic Bunch Graph Matching
(EBGM). Kepenekci proposes in (Kepenekci, 2001)
an algorithm that outperforms the classical EBGM.
Moreover, he addresses the main issue of the Elastic
Bunch Graph Matching, manual labeling of the land-
Recently, also the Scale Invariant Feature Trans-
form (SIFT) (Lowe, 1999) is utilized for face recog-
nition. The algorithm was originally developed for
object recognition. The SIFT features has the ability
to detect and describe local features in images. The
features are invariant to image scaling, translation and
rotation. Moreover, they are also partly invariant to
changes in illumination. When used in the AFR, the
SIFT feature vectors of the reference and test images
are compared using the Euclidean distance.
One of the first applications of this algorithm for
the AFR is proposed in (Aly, 2006). It takes the orig-
inal SIFT algorithm and creates the set of descriptors
as described in Section 3. Another approach called
Fixed-key-point-SIFT (FSIFT) is presented in (Krizaj
et al., 2010).
This section details the two AFR methods we used
in this work: the adapted Kepenekci method (Lenc
and Kr´al, 2012a) and the SIFT based Kepenekci ap-
proach (Lenc and Kr´al, 2012b).
3.1 Adapted Kepenekci Method
3.1.1 Gabor Filter
Gabor filter is a sinusoid modulated with a Gaussian.
A basic form of the two dimensional Gabor filter is
shown in equation 1.
g(x,y;λ,θ,ψ, σ,γ) = exp(
´x+ γ
+ ψ)
where ´x = xcosθ+ ysinθ, ´y = xsinθ+ ycosθ, λ is
the wavelength of the cosine factor, θ represents the
orientation of the filter and ψ is a phase offset, σ and
γ are parameters of the Gaussian envelope, σ is the
standard deviation of the Gaussian and γ defines the
ellipticity (aspect ratio) of the function.
The set of 40 Gabor filters with different frequen-
cies and orientations is used to extract local features.
We consider only the real part of the wavelet response.
3.1.2 Face Representation
The image is convolved with all Gabor filters from
the filter bank. 40 wavelet responses R
, where j =
1,...,40, are obtained. Each of these responses is
scanned with a sliding window. Assume a square win-
dow W of size w× w . All possible window positions
within the response are evaluated. The center of the
window, denoted (x
), is considered to be a fidu-
cial point iff:
) = max
(x,y) (2)
) >
wi hi
(x,y) (3)
where j = 1,...,40, wi and hi are image width and
height respectively.
The feature vector in point (x,y) is created as follows:
v(x,y) = {x,y, R
(x,y),..., R
(x,y)} (4)
The resulting vector thus contains information
about feature point coordinates and values of Gabor
responses in this point.
3.1.3 Face Comparison
The cosine similarity (Tan et al., 2005) is employed
for vector comparison. The similarity between two
vectors thus takes the values in interval [0,1]. Only
the last 40 positions in the vector are considered.
Let us call T a test image and G a gallery image.
For each feature vector t of the face T we determine
a set of relevant vectors g of the face G. Vector g is
relevant iff:
+ (y
< distanceThreshold (5)
If no relevant vector to vectort is found, vector t is
excluded from the comparison procedure. However,
the most similar vector (from the relevant vector set)
is used for the face similarity computation. The over-
all similarity of two faces OS is computed as an aver-
age of similarities between each pair of corresponding
vectors as:
= mean{S(t, g),t T,g G} (6)
Then, the face with the most similar vector to each
of the test face vectors is determined. The variable C
says how many times the gallery face G
was the clos-
est to some of the vectors of test face T. The similar-
ity is computed as C
where N
is the total number
of feature vectors in G
. Weighted sum of these two
similarities is used for similarity measure:
= αOS
+ β
The size of the sliding window is very important
for the performance of this method. It determines the
number of fiducial points detected and influences its
accuracy. The higher the window size the less fidu-
cial points are detected. On the other hand, search-
ing larger window needs more computation time. In
the comparison stage, the number of fiducial points
determines the time needed. The above mentioned
threshold distanceThreshold also influences the ac-
curacy and the run-time of this method. The smaller
the value of this threshold is, the less comparisons are
needed and the method works faster.
If more than one training example per person is
used, we create a so called “composed face”. It means
that all vectors extracted from the images of one per-
son are put together and the resulting set of vectors is
used as a gallery face.
3.2 SIFT based Kepenekci Method
The SIFT algorithm has basically four steps: ex-
trema detection, removal of key-points with low con-
trast, orientation assignment and descriptor calcula-
tion (Krizaj et al., 2010).
To determine the key-point locations, an image
pyramid with re-sampling between each level is cre-
ated. It ensures the scale invariance. Each pixel is
compared with its neighbors. Neighbors in its level
as well as in the two neighboring (lower and higher)
levels are examined. If the pixel is maximum or min-
imum of all the neighboring pixels, it is considered to
be a potential key-point.
For the resulting set of key-points their stability is
determined. Locations with low contrast and unstable
locations along edges are discarded.
Further, the orientation of each key-point is com-
puted. The computation is based upon gradient orien-
tations in the neighborhood of the pixel. The values
are weighted by the magnitudes of the gradient.
The final step is the creation of the descriptors.
The computation involves the 16 × 16 neighborhood
of the pixel. Gradient magnitudes and orientations are
computed in each point of the neighborhood. Their
values are weighted by a Gaussian. For each sub-
region of size 4× 4 (16 regions), the orientation his-
tograms are created. Finally, a vector containing 128
(16× 8) values is created.
The original SIFT algorithm is described in de-
tail in (Lowe, 1999; Lowe, 2004b) and (Krizaj et al.,
2010). An example of implementation of the SIFT
algorithm can be found in (Lowe, 2004a).
Face Comparison
To describe a face we create a set of the SIFT features.
For the face comparison the same matching scheme is
used as in the Kepenekci method 3.1.3. We use also a
composed face (see above) if more than one training
example per person is available.
3.3 Confidence Measure
As in many other works (Lleida and Rose, 1996;
Jiang, 2005), our confidence measure for automatic
face recognitionis an estimate of the a posteriori class
Let the output of our classifier be P(F|C), where
C is the recognized face class and F represents the
face features. The likelihoods P(F|C) are normalized
to compute the a posteriori class probabilities as fol-
P(C|F) =
F I M represents the set of all faces and P(C) denotes
the prior probability of the face class C.
The relative confidence value method as already
presented in (Lenc and Kr´al, 2011) is used. This ap-
proach computes the difference between the best hy-
pothesis and the second best one:
P = P(
C|F) max
(P(C|F)) (9)
Only the faces with P > T are accepted. This
approach aims at identifying the “dominant” faces
among all the other candidates. T is an acceptance
threshold and its optimal value is adjusted experimen-
4.1 Corpora
ORL Database
The ORL database was created at the AT & T Labora-
. It contains images of 40 individuals. 10 pic-
tures for each person are available. The images con-
tain just the face and have black homogeneous back-
ground. They may vary due to three following factors:
1) time of acquisition; 2) head size and pose; 3) light-
ing conditions. The size of the pictures is 92 × 112
pixels. The further description of this database is
in (Li and Jain, 2005).
Czech News Agency (
CTK) Database
This face data-set has been created using the algo-
rithm described in the next section. We created this
corpus in order to evaluate the AFR methods on real-
world data. All faces are extracted from the photo-
graphic data owned by the
Figure 1: Examples of one face from the
CTK face corpus.
The number of photographs containing human
faces reaches 2 millions whereas the count of indi-
viduals is about 20 thousand. Out of this amount we
created a sub-set containing faces of 1065 individu-
als. At least 4 images for each person are available.
The pictures size is 128× 128 pixels and have differ-
ent background. The number of the face examples per
person varies from 1 to 10 images.
Figure 1 shows an example of one person from
this corpus.
This corpus has some important characteristics:
large number of the different people,
time span between images of one individual is
more than 20 years,
pose, tilt and lighting conditions vary,
photos taken under real conditions,
difficulties for the automatic face recognition.
4.2 Corpus Creation Algorithm
Unfortunately, the labeled part of the
CTK corpus
contains not only the face in the photo (a whole per-
son, more faces, some background objects, etc. avail-
able). Therefore, the pre-processing of the photos
is necessary. In order to minimize the human costs,
an semi-automatic corpus creation algorithm is pro-
posed. It is composed of the following tasks:
1. face detection,
2. eyes detection,
3. head rotation,
4. image resizing and conversion to the grayscale,
5. identification and deletion of the incorrectly de-
tected faces,
6. manual verification
To detect and to extract the faces we used the
face detector implemented in the OpenCV library
http://opencv.willowgarage.com/wiki/. It uses the
well-known Viola-Jones (Viola and Jones, 2001) face
detection algorithm. Once the faces in the image are
detected, we try to correct the position and rotation
of the head. Using Viola-Jones boosted cascade clas-
sifier trained for eye detection we determine the posi-
tion of the eyes. If botheyes are successfullydetected,
we rotate and scale the face so that a horizontal posi-
tion of the eyes is fixed. If the detection of the eyes is
not successful the face is used as it is. Next, we resize
the faces to the size of the 128× 128pixel and convert
Figure 2: Examples of the incorrectly identified faces by the
OpenCV face detector.
it to the grayscale.
Unfortunately, the face detector identifies also a
certain amount (up to 50 % depending on the qual-
ity of images) of non-face images (false positives er-
rors, see Figure 2). These false detections must be
removed. We used a meta-classifier for this task in or-
der to distinguish two classes: face and non-face. We
chose a neural network of the type Multi-Layer Per-
ceptron (MLP) due to its simplicity and good classifi-
cation results in our previous experiments (Kr´al et al.,
2006). The proposed MLP has three layers contain-
ing 1024 (size of the input face vector), 10 and 2 (face
vs. non-face) nodes respectively. The input vector is
created by scanning the image by window of the size
4×4 pixels. An average intensity value in the window
is computed. To train the MLP, we used a set of man-
ually selected face and non-face (250 faces and 250
non-faces) examples. The resulting corpus has been
checked and corrected manually.
Figure 2 shows examples of false positives found
by the face detector.
4.3 Experiments
The following experiments are proposed and evalu-
ated in order to show:
1. the performance of the methods in the laboratory
2. a significant degradation of the recognition accu-
racy when the laboratory conditions are replaced
with the real ones,
3. the further degradation when the number of rec-
ognized individuals is increasing and the number
of the training examples is decreasing,
4. that the use of the confidence measure is benefi-
cial in order to filter out the incorrectly recognized
Both methods: adapted Kepenekci method (see
Section 3.1) and the SIFT based Kepenekci approach
(see Section 3.2) are evaluated on the two corpora de-
scribed above. The cross-validation procedure is used
to evaluate the approaches in all experiments.
Table 1: Recognition results on the ORL database.
Method Kepenekci SIFT
Training Ex. Recognition rate [%]
9 of 10 99.50 100
8 of 10 99.58 99.86
7 of 10 98.75 100
6 of 10 98.39 99.82
5 of 10 97.42 99.50
4 of 10 96.00 99.17
3 of 10 93.48 97.41
2 of 10 90.63 91.88
1 of 10 78.89 81.52
Accuracy in the Laboratory Conditions
Table 1 shows the recognitionrate of both methods on
the ORL database when different number of training
images (1-9) is used and the rest of images is used for
testing. This table shows that the recognition scores
of both approachesare comparableon the “small” and
“artificial” corpus. Moreover, the obtained recogni-
tion rate is close to 100% when more examples is
used. This experiment has been performed in order
to show that both approaches perform well in the lab-
oratory conditions on a small corpus.
Table 2: Recognition results on the
CTK database.
Method Kepenekci SIFT
Training Ex. Recognition rate [%]
9 of 10 60.81 72.70
8 of 10 57.66 69.07
7 of 10 53.83 65.20
6 of 10 50.10 62.36
5 of 10 47.12 57.21
4 of 10 42.88 51.17
3 of 10 37.55 44.21
2 of 10 32.09 34.57
1 of 10 24.62 22.37
Accuracy in the Real Conditions with a Small
Number of Individuals
Table 2 displays the results of the methods on a sub-
set of the real
CTK corpus of the comparable size as
the ORL database when the number of training exam-
ples varies from 1 to 9. The sub-set contains 37 indi-
viduals, 10 images for each person. This experiment
shows that the SIFT based Kepenekci approach out-
performs significantly the adapted Kepenekci method
(the difference about 11% when 9 training examples
used). Therefore, we use only the SIFT method for
further experiments.
Accuracy in the Real Conditions with a Huge
Number of Individuals
Table 3 details the relation among the recognition ac-
Table 3: Recognition results of the SIFT approach on the
CTK database using different number of training examples
and the different amount of individuals.
Training Ex. Database size (in-
dividuals #)
Recognition rate
9 of 10 37 72.70
8 of 9 88 56.70
7 of 8 194 49.94
6 of 7 367 41.92
5 of 6 595 33.51
4 of 5 841 27.90
3 of 4 1065 21.31
curacy, the number of the individuals and the num-
ber of the training examples. This table shows that
the recognition rate decreases significantly when the
number of individuals increases and the number of
training images decreases. Therefore, we use the con-
fidence measure (see Section 3.3) to post-process the
recognition results and to filter out the incorrectly rec-
ognized faces.
Confidence Measure
Two experiments with the confidence measure are re-
1. The number of individuals in the first experiment
is close to 200 people. This number is chosen be-
cause it is sufficient in the current version of our
AFR system for the needs of the
CTK. The num-
ber of training examples is 7, one example is used
for testing.
2. In the second experiment, we evaluate the method
on our whole corpus. The number of individuals
is thus 1065 people (3 training examples).
Figure 3 depicts the recognition rate and the num-
ber of the classified faces of the first experiment (194
different individuals). The recognition accuracy with-
out any confidence measure is only about 50%. This
figure shows, that the recognition accuracy is close to
100% when more than 20% of images is classified.
This result is interesting for our application because
the main accent is to the correct classification. The
amount of 20% of the correctly recognized faces rep-
resents a significant part of the corpus.
Figure 4 plots the recognition rate and the num-
ber of the classified faces of the second experiment
(1065 different individuals). Without any confidence
measure, the recognition rate is only 21%. This figure
shows, that the recognition accuracy is close to 80%
when more than 20% of images is classified. This re-
sult is not the perfect one. However, it is very promis-
ing and one of our future research directions focuses
thus on the proposition of better confidence measure
0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02
Number of classified faces [in %]
Recognition rate [in %]
Recognition rate
Figure 3: Face recognition rates and the numbers of the
identified faces when 7 of 8 training examples used (T
[0;1] and 194 individuals are recognized).
0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02
Number of classified faces [in %]
Recognition rate [in %]
Recognition rate
Figure 4: Face recognition rates and numbers of the identi-
fied faces when 3 of 4 training examples used (T [0;1]and
1065 individuals are recognized).
In this paper, we have presented the difficulties of
the AFR on the faces acquired in uncontrolled en-
vironment. Two previously proposed AFR meth-
ods (adapted Kepenekci method and the SIFT based
Kepenekci approach) are described and evaluated on
the ORL database (laboratory conditions) and on the
sub-set of the
CTK database (real-world corpus). We
have shown that the accuracy of both methods is
close to 100% on the ORL database. The results on
CTK database show that the SIFT based Kepenekci
method has significantly better recognition accuracy.
It reaches 72.7% whereas the adapted Kepenekci
method has recognition rate of 60.8% (9 training ex-
amples are used).
We further analysed the accuracy of the SIFT
based Kepenekci approach if the corpus size increases
and the number of training examples decreases. As
supposed, the recognition rate decreases with increas-
ing number of different recognized individuals. We
have also shown that the number of training examples
influences the accuracy significantly. While 9 train-
ing examples is used, the recognition rate is 72.7%. If
we use only 3 training examples the recognition rate
is only 21.3%.
In the last two experiments we employed the con-
fidence measure to post-process the recognition re-
sults. We compared the results when 7 training ex-
amples and 3 examples are used. The results show
that using confidence measure is very beneficial for
AFR under real-world conditions.
It is obvious that the AFR methods are nowadays
capable to recognize faces perfectly under the condi-
tion: the acquisition of the face images must be con-
trolled. If this condition is not accomplished, the task
is much more difficult. Therefore the perspectives of
the further work on recognition of real-world data lay
more in the detection step than in the recognition it-
self. Further increase of image quality will ensure
much better accuracy of the recognition. Using higher
quality images and utilising the confidence measure
will help to create a reliable recognition system.
This work has been partly supported by the UWB
grant SGS-2010-028 Advanced Computer and Infor-
mation Systems and by the European Regional De-
velopment Fund (ERDF), project NTIS - New Tech-
nologies for Information Society, European Centre of
Excellence, CZ.1.05/1.1.00/02.0090. We also would
like to thank Czech New Agency (
CTK) for support
and for providing the photographic data.
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