Genetic Algorithm for Weight Optimization in Descriptor based Face

Recognition Methods

Ladislav Lenc

1,2

Dept. of Computer Science & Engineering, Faculty of Applied Sciences,

University of West Bohemia, Plzeˇn, Czech Republic

NTIS - New Technologies for the Information Society, Faculty of Applied Sciences,

University of West Bohemia, Plzeˇn, Czech Republic

Keywords:

Face Recognition, Genetic Algorithm, Local Binary Patterns, LBP, Patterns of Oriented Edge Magnitudes,

POEM.

Abstract:

This paper presents a novel algorithm for weight optimization in descriptor based face recognition methods.

We aim at the local texture features that are currently very popular in the face recognition (FR) ﬁeld. Com-

mon concept in such methods is creating histograms of the operator values in rectangular image regions and

concatenating them into one large vector called histogram sequence (HS). Usually the facial regions are given

equal weight which does not correspond with the reality. We deal with this issue in this work and propose

a novel method that optimizes the weights of the regions. The optimization method is based on a genetic

algorithm (GA). We test the method together with the local binary patterns (LBP) and patterns of oriented

edge magnitudes (POEM) descriptors. We evaluate our algorithms on two real-world corpora: Unconstrained

facial images (UFI) database and FaceScrub database. The evaluation results show that the weighted methods

outperform the non-weighted ones. The best achieved scores are 68.93% on the UFI database and 57.81% on

the FaceScrub database.

1 INTRODUCTION

Face recognition (FR) methods based on local im-

age features are recently very popular among the

face recognition researchers. A signiﬁcant advan-

tage of these methods is a relatively easy computa-

tion and high recognition accuracy. These methods

usually outperform holistic approaches such as Eigen-

faces (Turk and Pentland, 1991) or Fisherfaces (Bel-

humeur et al., 1997).

There are a lot of various types of descriptors that

were utilized for FR. Among others we can mention

scale invariant feature transform (SIFT) (Lowe, 2004)

or speeded-up robust features (SURF) (Bay et al.,

2006). The SIFT and SURF methods proved very

good results in many computer vision tasks but the

use for FR is not as frequent. The majority of re-

searchers recently concentrate on Gabor based fea-

tures (Wiskott et al., 1997; Perez et al., 2011) and on

the local texture features such as local binary patterns

(LBP) (Ahonen et al., 2006). The newly designed

descriptors aim at improved ability to capture impor-

tant information contained in images. The matching

is then performed using a simple nearest neighbour

classiﬁer or can employ a more sophisticated one such

as support vector machines (SVM) or artiﬁcial neural

networks (ANN).

The local texture features proved very good per-

formance and therefore we chose this type of features

for our experiments. We concentrate on the match-

ing procedure of the image features created using the

well known algorithms such as LBP or patterns of ori-

ented edge magnitudes (POEM) (Vu et al., 2012). We

would like to extend the concept of weighting that was

proposed already in (Ahonen et al., 2006). In that

work a simple procedure of assigning the weights is

used. Image is split into rectangular regions of size

18× 21 pixels The training set is then classiﬁed based

on just one of the regions in that the image is parti-

tioned. The weight is assigned according to the recog-

nition score obtained using the particular window.

We believe that the weighting is very important

and can leverage the performance of descriptor based

methods. The main contribution of this work is thus

the proposal of a novel weight optimization proce-

dure based on a genetic algorithm (GA). Another im-

portant contribution is evaluation of the algorithm on

two real-world corpora. We chose the UFI and Face-

330

Lenc, L.

Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods.

DOI: 10.5220/0005704403300336

In Proceedings of the 8th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2016) - Volume 2, pages 330-336

ISBN: 978-989-758-172-4

Scrub (Ng and Winkler, 2014) databases. We evaluate

the identiﬁcation scenario which means we compare

the test face against gallery faces and search for the

most similar one. We use a scheme where the weights

are trained on one database and tested on the other.

The rest of the paper is organized as follows. Sec-

tion 2 summarizes important face recognition meth-

ods with a particular focus on the local image descrip-

tors. Several methods employing genetic algorithms

for optimization are also brieﬂy described. Section 3

details the LBP and POEM algorithms, the compari-

son procedure and the proposed genetic algorithm for

weight optimization. Section 4 reports the results of

evaluation on the UFI and FaceScrub corpora. In sec-

tion 5 we conclude the paper and give some possible

directions for further research.

2 RELATED WORK

Face recognition (FR) is nowadays a well established

ﬁeld in computer vision and image analysis. The ear-

lier methods were usually based on dimensionality re-

duction of image space using methods such as prin-

cipal component analysis (PCA) (Turk and Pentland,

1991) or linear discriminant analysis (LDA) (Bel-

humeur et al., 1997). These methods use the face im-

age as a whole and are usually called holistic. Other

large group of methods that gets more attention re-

cently is based on a set of image features.

LBP is one of the ﬁrst local texture features uti-

lized for face recognition. It was originally used for

texture representation as presented in (Ojala et al.,

1996). It is computed from the neighbourhood of

a given pixel and uses the intensity of the central pixel

as a threshold. The neighbouring pixels are compared

to that threshold and are assigned either 0 or 1 (lower

than threshold - 0, higher - 1). The binary digits are

concatenated to a binary number and then converted

to a decimal value. This value is used as a descriptor

of the pixel.

The ﬁrst application of LBP for face recognition

was proposed by Ahonen et al. in (Ahonen et al.,

2004). The face is divided into rectangular regions.

In each region a histogram of LBP values is com-

puted. All histograms are then concatenated into one

vector called histogram sequence (HS) which is used

for the face representation. The histogram intersec-

tion (HI) method or Chi square distance are used for

vector comparison. A weighted LBP modiﬁcation is

also proposed in this work. It gives more importance

to the regions around the eyes and the central part of

the face. The reported recognition rate on the FERET

dataset (Phillips et al., 1998) reaches 93% for the orig-

inal method and 97% for the weighted LBP method.

One of the extensions of LBP are dynamic thresh-

old local binary patterns (DTLBP) (Li et al., 2012). It

takes into consideration the mean value of the neigh-

bouring pixels and also the maximum contrast be-

tween the neighbouring points. According to the au-

thors, this variation is less sensitive to noise

Another extension of the original method are Lo-

cal Ternary Patterns (LTP) proposed in (Tan and

Triggs, 2010). It uses three states to capture the differ-

ences between the center pixel and the neighbouring

ones. Similarly to the DTLBP the LTP is less sensi-

tive to the noise.

The so called Local Derivative Patterns (LDP) are

proposed in (Zhang et al., 2010). In this case features

of higher order are computed. This descriptor thus

can represent more information than the original LBP.

The histogram for the original LBP contains 256

bins (8 bit binary number). However, it was proved

that approximately 90% of patterns belongs to a sub-

set of the values that are called uniform. The unifor-

mity means that there are at most two transitions from

0 to 1 or from 1 to 0 in the binary number. The number

of uniform patterns is 59 in case of 8-bit representa-

tion. The 59th bin is reserved for the non-uniform pat-

terns. This idea brings signiﬁcantly lower dimension

of resulting features whereas the descriptive abilities

are similar.

An interesting method utilizing uniform patterns

is proposed in (Yang and Wang, 2007). The authors

state that the histogram bin containing non-uniform

patterns dominates among other bins and gives thus

too much importance to this bin. Therefore they pro-

pose to assign such patterns to the closest uniform pat-

tern (according to Hamming distance).

One of more recent descriptors is POEM. It was

proposed in (Vu et al., 2012). It is based on the com-

putation of gradients in the image pixels. The gradi-

ent orientations are then quantized and accumulated

in a small region around the pixel called cell. The ac-

cumulated values are then compared with other cells

within a larger region called block. The histograms

are created for each direction in similar way as in

LBP. This approach achieves very good results on

FERET (97.6% for fb set) and LFW (75%) datasets.

Another interesting method proposed in (Vu,

2013) is called patterns of orientation difference

(POD). Whereas POEM encodes the differences in

edge magnitude distributions, POD encodes the dis-

tribution of orientations. It is assumed that this de-

scriptor gives complementary information to POEM

and the combination can improve the recognition re-

sults.

Local quantized patterns (LQP) (Hussain et al.,

Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods

331

2012) are a generalization of local features. It is based

on vector quantization and thus reduces the number of

patterns. Cosine similarity is used for face compari-

son. It is reported to achieve better results than other

local feature methods. The accuracy on the FERET

fb set is 99.9%.

A common property of the most of the above de-

scribed LBP methods is that the images are divided

into rectangular regions and histograms are computed

in each region. All histograms from one image are

concatenated and create the face representation. In

the comparison stage each histogram can have differ-

ent weight and thus can emphasize some important

face parts. We would like to employ GA for weight

optimization and therefore some GA approaches are

described next.

GAs are a class of algorithms belonging to the

group of evolutionary algorithms (EA). The idea was

proposed by Holland in (Holland, 1973) and GAs are

often used for optimization in many domains. It was

also relatively early adopted in the image processing

ﬁeld. The key concept of GAs is the Darwin’s theory

of natural selection. The ﬁttest specimens thus sur-

vive and create a better population in the next genera-

tion. Applying the rules of selection, crossover and

mutation it goes through the search space with the

goal to ﬁnd the optimal solution.

One interesting application of GAs in computer

vision is circle detection (Ayala-Ramirez et al., 2006).

It detects circles in the edge image of a scene. Each

candidate circle is described with its radius and coor-

dinates of the center. Fitness function evaluates the

real presence of the circle in the image. It is reported

that this approach is capable of detecting circles with

sub-pixel accuracy.

Another recent utilization of GA is color image

segmentation (Abbasgholipour et al., 2011). It uses

a permutation-coded genetic algorithm and aims at

segmentation under varying lighting conditions.

An example use of GA for face recognition is pro-

posed in (Al-Arashi et al., 2014). The GA was used

for PCA performance optimization in this work.

A survey of GA based methods can be found

in (Paulinas and Uˇsinskas, 2015).

3 PROPOSED METHOD

The method consists of three steps. The ﬁrst one is

feature extraction for which we employed the LBP

and POEM operators. The second step is the weight

optimization that determines the weights for the indi-

vidual regions in the image. Final step is classiﬁcation

that compares the image representations and ﬁnds the

Figure 1: An example of the feature computing by the orig-

inal LBP operator.

most similar image from the gallery.

3.1 LBP Operator

The LBP operator was proposed in (Ojala et al.,

1996). The original form uses a 3×3 neighbourhood

of the central pixel. The algorithm assigns either 0 or

1 value to the 8 neighbouring pixels by Equation 1.

N =



0 if g

< g

1 if g

≥ g

(1)

where N is the binary value assigned to the neigh-

bouring pixel, g

denotes the gray-level value of the

neighbouring pixel and g

is the gray-level value of

the central pixel. The resulting values are then con-

catenated into an 8 bit binary number. Its decimal

representation is used for further computation. This

approach is illustrated in Figure 1.

The original LBP operator was further extended to

use circular neighbourhoods of various sizes and also

with different numbers of points. A bilinear interpo-

lation is used to compute the values in the points that

are not placed in the pixel centres. The LBP is then

denoted as LBP

P,R

where P is the number of points

and R is the radius of the neighbourhood. The value

of the operator is computed by Equation 2.

LBP

P,R

P−1

∑

p=1

s(g

− g

, S(x) =



0 if x < 0

1 if x ≥ 0

(2)

where g

denotes the points on the circle and g

is the

central point. Figure 2 illustrates the computation of

LBP

8,2

operator.

3.2 POEM Operator

The POEM descriptor was proposed in (Vu et al.,

2012). First the gradient in each pixel of the input im-

age is computed. An approximation utilizing simple

convolution operator such as Sobel or Scharr is used

to compute gradients in the x and y directions. These

values are used for computation of gradient orienta-

tion and magnitude.

ICAART 2016 - 8th International Conference on Agents and Artiﬁcial Intelligence

332

Figure 2: Computation of LBP

8,2

operator.

Figure 3: Depiction of POEM computation (Vu et al.,

2012). Light gray square regions around pixels represent

the cells and larger surroundings with diameter L is called

block. Arrows represent the accumulated gradients.

The gradient orientations are then discretized. The

number of orientations is denoted d and is usually

set to 3. Each pixel is now represented as a vector

of length d. It is a histogram of gradient values in

a small square neighbourhood of a given pixel called

cell. Figure 3 depicts the meaning of cell and block

terms.

The ﬁnal encoding similar to that of LBP is done

in a round neighbourhood with diameter L called

block. The 8 cell values are compared with the cen-

tral one and the binary representation is created. It is

computed for each gradient orientation and thus the

descriptor is d times longer than in case of LBP.

3.3 Feature Vector Creation and

Comparison

The facial image is ﬁrst divided into N non-

overlapping rectangular regions. Histogram of op-

erator values is computed in each region. The di-

mension of the histogram depends on the operator

used. The individual histograms are ﬁnally concate-

nated into one large vector called histogram sequence.

We compare the representation using weighted χ

dis-

tance.

d(S, T) =

∑

i=1

∑

j=1

( j) − T

( j))

2(S

( j) + T

( j))

(3)

where S and T are two histogram sequences com-

posed from N histograms, w

is the weight assigned

to each histogram and L is the dimension of one his-

togram.

3.4 Weights Optimization

The optimal setting of weights of individual image re-

gions is the key contribution of this work. We employ

a genetic algorithm for this task. It is based on differ-

ential evolution.

We adopted the three main concepts commonly

used in GAs: selection, crossover and mutation. One

specimen is represented as a vector of weights for the

image regions. The length of the vector depends on

the size of the regions.

We begin with a randomly generated population

that is iteratively evolved and evaluated according to

the ﬁtness function given by equation 4.

hits

hits+ misses

(4)

whereC is a large constant determined experimen-

tally and hits and misses are the correctly and incor-

rectly classiﬁed examples respectively. nc is a value

that accumulates similarities of faces from different

classes. c is the accumulated value of similarities of

faces belonging to the same class.

4 EXPERIMENTAL SETUP

This section describes the corpora that we used for

evaluation and the results obtained on these datasets.

4.1 Corpora

We concentrate on challenging real-world corpora

that are recently used.

4.1.1 FaceScrub

This large database of real-world images was col-

lected using an approach described in (Ng and Win-

kler, 2014). It contains images of 530 individuals

equally split to males and females. The total number

of images is 107,818 with an average of 203 images

per person. The database is distributed as a list of

URLs where the images are stored. Each line in the

list contains the URL, name, coordinates of the facial

region and a checksum. The images thus have to be

downloaded and cropped according to the provided

information.

Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods

333

Figure 4: Example images from the FaceScrub database.

We downloaded and cropped the faces automati-

cally. Only the images with correct checksum were

downloaded. Some of the URLs were also not avail-

able in the moment of downloading. The resulting

dataset thus contains the 530 individuals but the to-

tal number of images is somewhat lower. It is 80,167

which gives an average of 151 images per person.

We didn’t ﬁnd any closer speciﬁcation of testing

protocol together with this dataset. Therefore, we di-

vided the database into two parts. Th ﬁrst one is used

as training set and the other one as test set. The divi-

sion was performed randomly. Figure 4 shows three

example images from this dataset.

4.1.2 Unconstrained Facial Images

The UFI database is composed of face images taken

in real-world conditions and is freely available for re-

search purposes at

http://ufi.kiv.zcu.cz

. It is

composed of two partitions. The ﬁrst one is called

Cropped images and contains automatically detected

faces from photographs. The number of individuals

is 605. These images are cropped and resized to have

approximately the same face size.

The second partition called Large images contains

larger image regions that contain not only the face.

Some background is also present and the face may

be located anywhere in the image. Therefore, it is

necessary to include the face detection task before the

face recognition itself. This partition contains images

of 530 individuals.

We used the Cropped images partition in our ex-

periments. The distribution of number of examples is

showed in ﬁgure 5.

We used the testing protocol designed by the au-

thors of the database. Figure 6 shows example images

from the UFI database.

4.2 Results

We evaluated our method on the two above described

corpora. The weight masks are always learned on the

other corpus to achieve unbiased results.

First, we tested the UFI database. To create the

masks, we used a portion of the FaceScrub database.

We chose just 5 images of one individual for the train-

ing set and other 5 for test set. We experimented with

1 2

5 6

100

200

300

278

Training examples

Number of individuals

Figure 5: Distribution of the training image numbers in the

Cropped images partition.

Figure 6: Example images from the UFI database.

3 different numbers of individuals (20, 50 and 100).

The resulting masks for LBP operator are depicted

in ﬁgure 7 while the masks for POEM operator are

shown in ﬁgure 8. The white parts have high weights

whereas the black ones low.

The obtained accuracies are reported in table 1.

The upper part shows results when the LBP opera-

tor is used while the lower part gives the results for

POEM operator. The ﬁrst line in each part gives the

accuracy obtained with the basic non-weighted vari-

ant of the method. The best results are in bold face.

The best score for LBP is obtained with the mask

trained on 100 individuals and reaches 54.71%. In the

case of POEM, the best accuracy 68.93% is achieved

with mask trained on 50 individuals.

The recognition accuracy for the FaceScrub

database is measured in a similar manner. We trained

the weight masks on different subsets of the UFI

database. We chose 50, 100 and all (605) individu-

Figure 7: Weight masks trained on the FaceScrub database

and LBP operator with 20, 50 and 100 individuals (from left

to right).

ICAART 2016 - 8th International Conference on Agents and Artiﬁcial Intelligence

334

Figure 8: Weight masks trained on the FaceScrub database

and POEM operator with 20, 50 and 100 individuals (from

left to right).

Figure 9: Weight masks trained on the UFI database and

LBP operator with 50, 100 and all individuals (from left to

right).

Figure 10: Weight masks trained on the FaceScrub database

and POEM operator with 50, 100 and all individuals (from

left to right).

als. The learned masks for LBP and POEM operators

are depicted in ﬁgure 9 and ﬁgure 10 respectively.

Table 2 shows the accuracies obtained on the

FaceScrub database. The upper part shows results for

LBP and the lower one for POEM. The best results

are in bold face.

The highest accuracy for LBP operator is 47.52%

when the mask is trained on the whole UFI database.

The best result for POEM is 57.81% also with the

mask trained on all individuals from UFI. The results

indicate that the test scheme we used for FaceScrub

database is even more challenging than the one used

for UFI database.

5 CONCLUSIONS AND

PERSPECTIVES

In this paper we proposed a novel method for weight

optimization in the descriptor based face recognition

methods. It is based on a genetic algorithm and tries

to determine optimal weights on a set of facial im-

Table 1: Recognition results on the UFI database.

Recognition rate [%]

LBP non-weigted 49.26

LBP mask-20 52.73

LBP mask-50 54.55

LBP mask-100 54.71

POEM non-weighted 65.95

POEM mask-20 68.43

POEM mask-50 68.93

POEM mask-100 67.60

Table 2: Recognition results on the FaceScrub database.

Recognition rate [%]

LBP non-weighted 44.27

LBP mask-50 42.34

LBP mask-100 43.66

LBP mask-all 47.52

POEM non-weighted 56.35

POEM mask-50 56.60

POEM mask-100 56.60

POEM mask-all 57.81

ages We used the optimized weights together with the

LBP and POEM descriptors. The method is tested

on two challenging real-world corpora, namely Face-

Scrub and UFI. We show that the results are promis-

ing and outperform the basic non-weighted methods.

The accuracy on the UFI database shows improve-

ment of 5.4% with the LBP operator and 1.7% with

the POEM operator compared to the non-weighted

methods. In the case of FaceScrub database the im-

provement is 3.2% with LBP and 1.5% with POEM.

The proposed method have many possibilities for

further improvement. One of them is to improve the

initial conditions of the GA. The start weights could

be possibly assigned according to a Gaussian distribu-

tion which would emphasize the central parts of the

face. Also the ﬁtness function can be probably im-

proved.

ACKNOWLEDGEMENTS

This work has been supported by the project LO1506

of the Czech Ministry of Education, Youth and Sports.

Special thanks belong to Vojtˇech Koˇsaˇr for implemen-

tation and valuable ideas. We also would like to thank

Czech New Agency (

CTK) for support and for pro-

viding the photographic data.

REFERENCES

Abbasgholipour, M., Omid, M., Keyhani, A., and Mo-

htasebi, S. (2011). Color image segmentation with

Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods

335

genetic algorithm in a raisin sorting system based on

machine vision in variable conditions. Expert Systems

with Applications, 38(4):3671–3678.

Ahonen, T., Hadid, A., and Pietik¨ainen, M. (2004). Face

recognition with local binary patterns. In Computer

vision-eccv 2004, pages 469–481. Springer.

Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face

description with local binary patterns: Application to

face recognition. Pattern Analysis and Machine Intel-

ligence, IEEE Transactions on, 28(12):2037–2041.

Al-Arashi, W. H., Ibrahim, H., and Suandi, S. A.

(2014). Optimizing principal component analysis per-

formance for face recognition using genetic algorithm.

Neurocomputing, 128:415–420.

Ayala-Ramirez, V., Garcia-Capulin, C. H., Perez-Garcia,

A., and Sanchez-Yanez, R. E. (2006). Circle detection

on images using genetic algorithms. Pattern Recogni-

tion Letters, 27(6):652–657.

Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf:

Speeded up robust features. In Computer vision–

ECCV 2006, pages 404–417. Springer.

Belhumeur, P. N., Hespanha, J. P., and Kriegman, D. (1997).

Eigenfaces vs. ﬁsherfaces: Recognition using class

speciﬁc linear projection. Pattern Analysis and Ma-

chine Intelligence, IEEE Transactions on, 19(7):711–

720.

Holland, J. H. (1973). Genetic algorithms and the opti-

mal allocation of trials. SIAM Journal on Computing,

2(2):88–105.

Hussain, S. U., Napol´eon, T., and Jurie, F. (2012). Face

recognition using local quantized patterns. In British

Machive Vision Conference, pages 11–pages.

Li, W., Fu, P., and Zhou, L. (2012). Face recognition

method based on dynamic threshold local binary pat-

tern. In Proceedings of the 4th International Confer-

ence on Internet Multimedia Computing and Service,

pages 20–24. ACM.

Lowe, D. G. (2004). Distinctive image features from scale-

invariant keypoints. International journal of computer

vision, 60(2):91–110.

Ng, H.-W. and Winkler, S. (2014). A data-driven approach

to cleaning large face datasets. In Image Process-

ing (ICIP), 2014 IEEE International Conference on,

pages 343–347. IEEE.

Ojala, T., Pietik¨ainen, M., and Harwood, D. (1996). A com-

parative study of texture measures with classiﬁcation

based on featured distributions. Pattern recognition,

29(1):51–59.

Paulinas, M. and Uˇsinskas, A. (2015). A survey of genetic

algorithms applications for image enhancement and

segmentation. Information Technology and control,

36(3).

Perez, C. A., Cament, L. A., and Castillo, L. E. (2011).

Methodological improvement on local gabor face

recognition based on feature selection and enhanced

borda count. Pattern Recognition, 44(4):951–963.

Phillips, P. J., Wechsler, H., Huang, J., and Rauss, P. (1998).

The FERET database and evaluation procedure for

face recognition algorithms. Image and Vision Com-

puting, 16(5):295–306.

Tan, X. and Triggs, B. (2010). Enhanced local texture fea-

ture sets for face recognition under difﬁcult lighting

conditions. Image Processing, IEEE Transactions on,

19(6):1635–1650.

Turk, M. A. and Pentland, A. P. (1991). Face recogni-

tion using eigenfaces. In Computer Vision and Pat-

tern Recognition, 1991. Proceedings CVPR’91., IEEE

Computer Society Conference on, pages 586–591.

IEEE.

Vu, N.-S. (2013). Exploring patterns of gradient orienta-

tions and magnitudes for face recognition. Informa-

tion Forensics and Security, IEEE Transactions on,

8(2):295–304.

Vu, N.-S., Dee, H. M., and Caplier, A. (2012). Face recog-

nition using the poem descriptor. Pattern Recognition,

45(7):2478–2488.

Wiskott, L., Fellous, J.-M., Kuiger, N., and Von Der Mals-

burg, C. (1997). Face recognition by elastic bunch

graph matching. Pattern Analysis and Machine Intel-

ligence, IEEE Transactions on, 19(7):775–779.

Yang, H. and Wang, Y. (2007). A lbp-based face recognition

method with hamming distance constraint. In Image

and Graphics, 2007. ICIG 2007. Fourth International

Conference on, pages 645–649. IEEE.

Zhang, B., Gao, Y., Zhao, S., and Liu, J. (2010). Lo-

cal derivative pattern versus local binary pattern: face

recognition with high-order local pattern descriptor.

Image Processing, IEEE Transactions on, 19(2):533–

544.

ICAART 2016 - 8th International Conference on Agents and Artiﬁcial Intelligence

336