International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
5
A Novel Method for Rotational and Pose Invariant Child
orAdult Age Classification based on Morphological
Pattern Representation Schemes
1
Dr. Pullela S V V S R Kumar, 2 Ms. A V Lakshmi Dhanisetty, 3Dr, U Ravi Babu
1
Professor of CSE, Sri Aditya Engineering College, Surampalem, pullelark@yahoo.com
*2
Computer Science & Engineering, Sri Aditya Engineering College,
Email ID : srilakshmimca4@gmail.com
3,
Professor, Dept. of CSE,MREC(A), India, uppu.ravibabu@mrec.ac.in
Abstract-- The present paper proposes an approach for classify
the facial images either child or adult based on local texture
features extracted on facial images. The local texture features
used in this paper are Morphological-prehistoric Patterns with
grain components (MP-g). these patterns are extracted on a Local
Directional Pattern (LDP) values. Edge response values in all
eight directions from a 3×3 local window are used to calculate the
LDP values. The local descriptor LDP is more constant in the
incidence of noise and lighting changes, since edge response level
is more stable than pixel intensity. The proposed method is
rotationally& poses invariant when compared to pattern trends
that represents a shape. The proposed method is tested on large
set of images from different databases like FgNet, Morph, Google
and scanned images. The present paper proves the efficiency of
the proposed method.
Indexed Term— Morphological Prehistoric Patterns, Local
Directional Pattern, invariant, pose invariant.
1. INTRODUCTION
Computer vision and psychophysics researchers faced so
many challenges by face recognition of human and
representation the typical features of human faces. Human
faces be in the right place to a 3-D objects. Therefore, to
develop an accurate representation for description that
consider for lighting; face variations, facial expressions, etc.;
of facial images. In face recognition system, large database
maintained and search an image in that database. Day to day
increase the size of the database and also increase the
computational cost for the classification of human face into
two categories i.e. either child or adult.
To address this problem some procedures that highlights
the important of facial development over a period of instance
and considered a representation to minimize the dissimilarity
between testdatabasesand images which are used at the time of
method development. In appearance-based method, a face
image is usually considered as a point in the high-dimensional
space. Many linear subspace learning methods, such as
LDA/FKT (linear discriminant analysis/Fukunaga–Koontz
transform) [6], Eigenface [1, 2], C-LDA (complete LDA) [4],
Fisherface [3], MMSD (multiple maximum scatter difference)
[5], and Laplacian face [7] are typical dimensionality
reduction methods to find a low-dimensional feature space.
First,Local Binary Pattern LBP operator was introduced by
Ojala et al. [8] for texture categorization and texture analysis.
The LBP given good results especially in texture analysis so
that LBP mostly suitable in texture examination and its
applications. The LBP operator has hugeacceptance against
lighting changes. The main advantage of LBP is, It was
obtained with sample calculations. From the above properties,
it is well suitable for real-world applications like image texture
analysis. The concept of LBP was also extended in
applications such as face recognition and age classification [9,
10,11].
In above proposed methodsareapplied on small set images and
get comparative results but those methods are not suitable for
all kinds of databases. The proposed method is applied on
large set of images and different kinds of databases. No such
method is available to classify the facial images into two
categories i.e. either child or adult in effective and efficient
manner of different database like FgNet, Morph Google and
scanned image databases.
The paper is organized as follows: Section 2 explains the
proposed method and result analysis, experimental details and
discussions are explained in Section 3. Conclusion part are
given in the section 4.
2. T HE PROPOSED MORPHOLOGY P REHISTORIC P ATTERNS
WITH G RAIN COMPONENTS ON LDP FOR CHILD AND ADULT
AGE C LASSIFICATION
The present paper identified MP-g based on changes in
the facial skin. The skin of a human face tends to more alters
with growing age is observed by the present method. These
changes in the skin are subjugated by MP-g. The other
important feature of the present method is to find which MP-g
causes, the quick changes in the skin that leads to a higher age
classification rate. The proposed novel scheme contains
fivemajor steps. In the first step, the facial color image is
cropped to cover only skin area of the face. Convert the RGB
into gray scale image in 2nd step. Gray level facial image is
converted into a two valued image by using LDP approach in
the 3rdstep. In the fourthstep, identify the Morphological
prehistoric patterns with one to eight-grain components (MP1g to MP-8g). In the final step, derive an algorithm for
classifying the facial image into either child or adult. The
schematic diagram of the entire process is illustrated in figure
1.
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International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
6
Cropping
Original
Facial image
Cropped
Image
Binary facial
Image
Identify MP-1g to
MP-8g patterns
Convert RGB
to Gray
LDP with
RobinCompass
MAsk
Classification
Fig. 1. Block diagram of the child and adult classification system
Step 1: Cropping: In this proposed method cropping is
necessary because of identifying the facial skin edges. The
proposed method based on the skin area of the human face.
The given database, each facial image consist not only skin
area but also hair, neck, and other areas also included. So to
remove the unwanted area from the facial image cropping is
necessary for effective results. The input facial image is
cropped to cover the entire skin area of the face based on the
location of two eyes in the first step as shown in figure 2.
(a)
(b)
Fig. 2.Crop operation a) Input image b) Output image after cropping
Step 2: RGB to Gray scale conversion:
So many color models are available in color image processing.
The facial image is converted to gray scale image for
identifying the morphological patterns on the facial color
image. The present paper uses HSV color model for
converting the facial color image into gray scale, because the
present study is aimed to classify the human age into four
groups with a gap of 15 years based on changes on facial skin
are identified on gray scale image.HSV color space separates
the color into three categories i.e. hue, saturation, and value.
Separation means variations of color are observed
individually. The convertingequations for RGB to grey level
conversion are given below.
(
)
(1)
(
)
S=
(2)
(3)
(4)
(5)
In this work, the color component Hue (H) is considered as
grey information for the classification of facial images. And
this value is lies between 0 and255.
Step 3: Local Directional Pattern
Local Directional Pattern (LDP) [12] concept is used in
this present study because it has more advantages compare
toLBP approach. The LDP approach is more suitable for age
group classification because this approach considers the edge
response values in all different directions instead of
surrounding neighboring pixel intensities like LBP. This
provides more consistency in the presence of noise, and
illumination changes since edge response magnitude is more
stable than pixel intensity. The LDP is based on LBP. In the
LBP operator, a gray-scale invariant texture prehistoric, has
gained significant popularity for describing the texture [13]. It
labels each pixel of an image by thresholding its Pneighboring values with the center value by converting the
result into a binary number by using Equation 6.
p 1
LBPP ,R ( xc , yc )   s( ESP  CP)2 p ,
p 0
1 x  0
s ( x)  
0 x  0
(6)
Where CP denotes the gray value of the center pixel (xc, yc)
and ESP corresponds to the gray values of P equally spaced
pixels on the circumference of a circle with radius R.
Local Directional Pattern with Robin Compass Masks
Response
The LDP generates eight-bit binary code assigned to each
3×3 sub window of an input image. These patterns are
calculated by comparing the relative edge response value of a
pixel in different directions by using Robin compass masks.
The Robin compass masks in eight distinct orientations (r0~r7)
centered on its own position. The Robin compass masks are
shown in the Fig.3.
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International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
7
Fig. 3.Robin Compass masks in eight directions.
Applying Robin masks on 3×3 masks, eight mask values
V0, V1, …,V7 are obtained, each representing the edge
significance in its respective direction. The mask values are
not equally important in all directions. In robin mask values
corner or edge pixels show higher values because those pixels
are more important in particular direction.
The LDP code produces the more firm pattern in the
presence of noise, illumination changes and various
(a)
26
38
85
10
50
53
48
32
60
LBP=00111000
LDP=00010011
conversion schemes of color facial images into gray images.
For example, Fig. 4 shows an original image and the
corresponding image with illumination changes. After
illumination change, 5th bit of LBP changed from 1 to 0, Thus
LBP pattern changed from uniform to a non-uniform code.
Since gradients are more stable than gray value, LDP pattern
provides the same-pattern value even in the presence of noise
and non-monotonic illumination changes.
26
38
85
10
50
49
48
32
6
LBP=00101000
LDP=00010011
(b)
Fig.4.Calculating the LDP and LBP values in two different situations (a) Original Image (b) Image with Noise
Step 4: Evaluation of Morphological-prehistoric Patterns
with Grain Components (MP-g) on LDP
On the binary LDP facial texture images of the previous
step, the present study evaluated the incidence of MP-g on a
3×3 mask. The present study classify the facial images into
either Child or adult based on the number of grains incidence
on a 3×3 sub-window LDP facial image in any orientation.
Thismakes the present method as rotationally and poses
invariant. The present method countsfrequency incidence of
MP-g if and only if the central pixel of the 3×3 window is 1
and it is treated as a grain. If the central pixel is zero (0) then
3×3 window is treated as not-a-grain,In the following figures
„0‟ indicates no grain and „1‟ indicates a grain. There can be
eight combinations of MP-1g, which are shown in the Fig. 5.
By any rotation, the MP-1g may change its position in 8 ways
0
0
0
0
1
0
(a)
0 0
1 1
0 0
(e)
0
1
0
0
0
0
0
0
0
0
1
0
(b)
1 0
0 1
0 0
0
0
1
0
0
0
on a 3×3 mask as shown in Fig.5. The present method counts
the frequency incidence of MP-1g on a 3×3 mask irrespective
of its position. Therefore, the present method is rotationally
invariant. There will be seven different formations of MP‟s
with 2pixel-grain components (MP-2g) by fixing one of the
grains at pixel location (0,0) on a 33 mask as shown in Fig.
6. In a similar way, there will be six formations of MP-2g by
locating one of the grains at the pixel location (0,1) as shown
in Fig. 7. Thus, there will be 7! ways of forming MP-2g for a
3x3 window. In the same way, there will be 6!, 5!, 4!, 3!, 2!
and 1! ways of forming MP-g of 3, 4, 5, 6, 7 and 8
respectively, on a 3x3 mask irrespective of their rotational
positions.
0
0
0
0
1
1
( c)
0 1
0 1
0 0
0
0
0
0
0
0
(f)
(g)
Fig. 5. Representation of 3×3 window withMP-1g.
153805-7474-IJECS-IJENS © October 2015 IJENS
0
0
1
0
1
0
(d)
0 0
0 1
0 0
0
0
0
1
0
0
(h)
IJENS
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
1
0
0
0
1
0
0
1
0
1
0
0
(a)
0
1
0
0
0
1
1
0
0
(b)
1
1
0
0
1
0
0
0
0
0
1
1
0
0
0
1
0
1
( c)
1
0
0
(e)
1
1
0
0
0
0
0
1
0
8
0
0
0
(d)
1
0
0
(f)
0
1
0
1
0
0
(g)
Fig. 6. Representation of MP-2g by fixing grain at (0,1)
0
0
0
1
1
0
(a)
0 1
0 1
1 0
0
1
0
0
0
0
0
0
0
1
1
0
(b)
0 1
1 1
0 0
0
0
1
0
0
0
0
0
0
1
1
1
( c)
0 1
0 1
0 0
0
0
0
1
0
0
(d)
(e)
(f)
Fig. 7. Representation of MP-2g by fixing one of the grain component at (0,1).
The frequency incidence of MP-1g to MP-8g are
computed by the proposed Algorithm 1 on a 33 nonoverlapped mask of LDP facial texture images, and stored in
the facial database.
The pseudo code entire method is illustrated below
Step 1: Read input image
Step 2: Crop the facial image
Step 3: if the input image is RGB then covert the image into
gray level image as described by the step2 in section2
with the size of N×N
Step 4: Define the RobinMasks(r0, r1, r2, r3, r4, r5, r6, r7 ) based
on masks given in the figure 3.
Step 5: Convert the Gray image into Binary by using LDP
Robin Masks
(a) Apply 2-D convolution of image G(i,j) and mask
MN(i,j) with size 3×3 F(i,j)= G(i,j)*Mn(i,j)
(b) Calculate the threshold, th= F(i+1,j+1)
(c) Initialize the variables p=0 and q=0
(d) ifF(p,q)>=thenBW(p,q)= 1 otherwise BW(p,q)= 0
(e) Initialize the variables l=0 and m=0
(f) for a = l to l+2 and b = m to m+2 do the following
procedure
(i) if BW(l+1,m+1)==1 then Count the frequencies of
incidence of Grain Features from 1,2,…,8
otherwise update m = m+3
(ii) ifm< N then go to step (f) otherwise update l =
l+3 and m = 0
(iii) if l< N then go to step (f) otherwise Store the
Grain features in the face recognition database
and stop the procedure
3. RESULTS AND D ISCUSSION
To find the consequence of the present method, the
present study has evaluated MP-1g to MP-8g on facial LDP
images of child and adults from different poses of 1002 FgNet
ageing databaseimages, 12000Morph data base images, 1500
scanned images and 750 Google imagestotally, its leads of
15252 images. Some of the child and adult images in different
databases are shown in figure8 and Figure9. The present
study considered that the childhood agesbetween 0 and 15
years and adulthood is over30 years.
Table 1 and Table 2 indicate the frequency incidence of
MP-g for sample database of FgNet. The facial image is
recognized as child or adult based on Algorithm 1. From the
computed frequency of incidence of MP-1g to MP-8g by the
Algorithm 1 the present study observed that only twoMP-gS
are exhibiting successful child-adulthood classification rates.
The MP-1g and MP-4g have proved to have significant,
precise and accurate classification rates than others MP-g‟s.
The present study suggests that it is not necessary to evaluate
frequency incidence of MP-2g, MP-3g, MP-5g, MP-6g, MP7g and MP-8g on facial LDP images of the child and adult for
classification. Since the facial images are of different poses,
the proposed method is posed invariant. To prove the
proposed method is rotationally invariant the MP-g are
evaluated with different rotations 30, 45 and 135 degrees
153805-7474-IJECS-IJENS © October 2015 IJENS
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International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
andthe obtained results are scheduled in Tables from 3 to 8.
Even by rotation with different angles, the Algorithm 1 based
on MP-1g and MP-4g classifies the child and adult. This
proves that the present method is rotationally invariant. Thus,
9
the present method has overcome the disadvantage of pattern
based and also previous methods that are rotational and pose
variant.
Fig. 8. Sample facial images of adults with different poses of FgNet aging database.
Table I
Frequency incidence of child facial images using MP-g.
Child images
015A01
002A03
009A03
069A03
053A06
066A06a
019A07
016A08
023A09
073A09
065A09
011A11
022A11
012A12
008A13
Sci-img-01
Sci-img-02
Sci-img-03
Sci-img-04
Sci-img-05
mor-img-01
mor-img-02
mor-img-03
mor-img-04
mor-img-05
MP-1
14689
15365
13649
13451
14478
19661
14369
17206
21243
10871
11821
18020
22196
16829
14843
13568
14768
15649
18637
19037
17433
15684
13081
16566
14595
MP-2
765
864
752
1468
1090
1170
1092
788
1459
1186
806
958
789
923
1421
963
768
854
913
963
876
961
1293
620
926
MP-3
679
837
913
1188
861
929
982
658
990
1164
661
687
799
690
1046
914
863
786
597
618
723
831
1176
640
726
MP-4
964
952
994
1228
1052
1138
1163
1009
1183
1122
856
919
910
1023
1162
984
913
927
1013
1019
1101
978
1164
917
935
MP-5
1047
964
899
2209
1233
1454
1676
833
1129
2258
930
733
1092
854
1305
865
1025
973
918
879
1123
1079
2372
1065
1153
153805-7474-IJECS-IJENS © October 2015 IJENS
MP-6
2015
2135
2049
4066
1763
2221
3527
1216
1521
4396
1774
1153
1325
9080
1389
1423
1564
1579
1679
2123
2094
1998
4605
2490
2255
MP-7
1371
1129
1078
2155
1186
1379
2956
363
774
3901
1501
611
863
617
746
865
1365
1278
1135
1496
1526
1428
2996
2168
2626
MP-8
2076
3168
7045
895
548
828
3015
138
581
2482
10531
230
706
195
299
203
276
536
895
9563
8768
1963
1493
4414
5664
IJENS
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
10
Table II
Frequency incidence of adult facial images using MP-g.
Adult images
027A41
062A41
071A42
033A44
047A45
028A46
045A48
003A49
048A50
039A52
004A53
005A61
006A61
Sci-img-101
Sci-img-102
Sci-img-103
Sci-img-104
Sci-img-105
mor-img-501
mor-img-502
mor-img-503
mor-img-504
mor-img-505
MP-1
13658
15638
17698
18486
22360
20731
18649
20134
23498
16538
18649
16498
15253
22308
20125
21237
19858
14628
19556
14390
19679
20860
23198
MP-2
1075
1349
1745
2146
1140
861
1948
1956
896
2313
2013
2146
1594
1374
2237
2379
1290
1643
1065
2159
2140
1351
1298
MP-3
865
913
1035
1583
924
760
964
813
854
2169
979
1237
1477
981
1165
1179
1067
1629
910
1912
1204
952
1076
MP-4
1364
1268
1495
2037
1273
1182
2018
2134
1204
2406
1769
1649
2057
1437
1548
1788
1621
1936
1278
2513
1499
1771
1254
MP-5
1123
1235
1465
2140
1088
1368
2013
2348
1065
2635
1645
2156
2424
1105
1652
1847
1569
2414
1395
3067
1769
1637
1286
MP-6
1450
3623
2359
2855
1400
2120
1365
1433
1637
3013
1956
1765
3758
1233
2016
2347
2188
4082
2373
4187
2246
2585
1654
MP-7
1211
1466
1355
923
514
1011
1217
1175
885
1143
1259
1567
1534
251
1468
1346
852
2934
1587
1770
1646
998
586
MP-8
384
295
406
406
281
647
594
712
337
359
535
478
783
191
776
649
435
1310
716
578
662
522
224
Algorithm 1: Rotational and pose invariant child and adult age classification using MP-g on LDP.
if (MP-1g<=13700)
print (facial image is of Child)
elseif (((MP-1g >14000) && (MP-1g <22300)) && ( MP-4g<=1200))
print (facial image is of Child)
elseif (((MP-1g >14000) && (MP-1g <23500)) && ((MP-4g >1300) && (MP-4g < 2350)))
print (facial image is of the Adult)
else
print (facial image is not of the child and the adult)
end
The Algorithm 1 classifies child from adult, based only on the
14000 to 23500 and a MP-4g count is less than 1300 then it
frequency incidence of MP-1g and MP-4g values. If a MP-1g
classifies as a child otherwise adult as specified in the
value is less than 13700 then the facial image is treated as
Algorithm 1. The same thing is also true for all rotational
child, else they form group 2 entries. By considering both
facial texture images.
MP-1g and MP-4g values if a MP-1g count is inbetween
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International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
11
Table III
Frequency incidence of a MP-g with 300 rotation on LDP for child facial images.
Child images
002A03
008A13
009A03
011A11
012A12
015A01
016A08
019A07
022A11
023A09
073A09
069A03
066A06a
065A09
053A06
Sci-img-01
Sci-img-02
Sci-img-03
Sci-img-04
Sci-img-05
mor-img-01
mor-img-02
mor-img-03
mor-img-04
mor-img-05
MP-1
10346
9768
10769
15722
16014
7568
8316
9769
10325
12356
6106
14389
6139
7024
11737
13645
16819
13971
11261
11455
8301
14290
9658
8923
13257
MP-2
613
718
834
794
1268
513
648
716
839
746
1029
911
685
539
480
943
852
1431
984
585
1149
950
579
732
1595
MP-3
634
728
531
562
888
1365
1230
1065
943
1032
1094
785
734
517
471
978
662
1085
788
876
1227
751
528
567
1287
MP-4
1064
978
867
637
938
846
813
679
681
1035
1138
932
1010
617
743
1094
842
1169
999
1113
1004
992
663
703
1145
MP-5
1348
1346
927
774
889
1035
778
684
1235
1365
2230
1199
1349
756
982
1649
994
1196
1179
1869
2370
1268
673
765
2333
MP-6
2216
2575
1567
799
914
936
1365
1364
2346
2867
2255
1224
1374
781
1007
3121
1019
1221
1204
2348
2395
1293
698
790
2358
MP-7
2349
2659
1946
520
574
694
1346
1864
1964
2034
3884
2624
2439
1371
2062
2264
527
717
1038
2019
2722
1123
364
295
2002
MP-8
16494
13462
12346
18924
20005
19864
23156
23458
22467
19467
22269
31622
17932
30261
24097
18631
20343
19158
17023
17345
21617
20056
12336
11841
25244
MP-7
1425
1268
1361
670
580
1345
1227
1278
3038
1994
295
419
1070
1236
943
1600
957
1649
1721
1833
1629
1267
603
MP-8
23456
22658
31456
32992
30986
20349
27725
25648
31314
21546
24098
24834
27388
20658
33341
33123
35323
26690
27988
26485
24845
26921
26817
Table IV
Frequency incidenceof aMP-g with 300 rotation on LDP for adult facial images.
Adult images
003A49
005A61
006A61
027A41
028A46
033A44
039A52
045A48
047A45
048A50
071A42
062A41
004A53
Sci-img-101
Sci-img-102
Sci-img-103
Sci-img-104
Sci-img-105
mor-img-501
mor-img-502
mor-img-503
mor-img-504
mor-img-505
MP-1
11235
13465
12346
17960
18893
13495
15144
10395
10970
12586
18556
18032
14167
13644
15264
9034
11630
10349
12453
11358
11173
14159
14129
MP-2
1358
1649
1346
812
1196
574
2391
974
1526
618
1167
1081
833
529
1137
2093
1679
936
817
928
1693
916
1234
MP-3
1265
1349
1765
1326
1280
1270
2726
1794
3704
1873
910
1110
1972
1979
1905
3372
1973
1864
1945
2034
3195
1957
1716
MP-4
1547
1463
1429
1823
1399
2214
2318
1358
1850
2199
1296
1246
1967
2035
1213
2285
1607
1647
1950
1713
1942
1999
1339
MP-5
1649
1563
1429
1034
1255
1099
2624
1037
2327
1267
1155
1008
1274
1348
1471
2930
1783
945
865
765
2371
1343
1530
153805-7474-IJECS-IJENS © October 2015 IJENS
MP-6
2065
2164
2318
652
980
1649
2114
2495
1540
1765
888
835
694
1649
995
1832
1317
2020
1367
1428
1517
803
997
IJENS
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
12
Table V
Frequency incidence of a MP-g with 450 rotation on LDP for child facial images.
Child images
002A03
008A13
009A03
011A11
012A12
015A01
016A08
019A07
022A11
023A09
073A09
069A03
066A06a
065A09
053A06
Sci-img-01
Sci-img-02
Sci-img-03
Sci-img-04
Sci-img-05
mor-img-01
mor-img-02
mor-img-03
mor-img-04
mor-img-05
MP-1
12649
7510
13432
9468
12466
15160
9846
11235
8697
10356
5228
8764
13568
14665
12356
13091
8091
7264
6497
14763
15992
10398
5298
6109
10904
MP-2
815
1243
946
798
1638
1270
575
1035
661
897
1067
596
929
1123
946
1475
714
613
678
784
865
1030
660
592
485
MP-3
1344
1294
826
1037
1397
873
613
1175
535
1239
1183
915
837
1094
1211
1121
630
846
769
609
691
829
740
554
526
MP-4
611
1035
1065
864
1183
914
865
599
579
798
1194
1403
981
764
849
1181
735
943
1097
665
813
1013
1035
618
757
MP-5
1214
2210
1115
1679
2153
864
894
2133
601
2041
2169
1035
1176
1449
1365
1126
642
1863
1486
742
933
1062
1347
736
901
MP-6
1458
2766
1169
1864
2008
731
1649
2213
274
1946
3836
1764
2537
2103
2317
881
403
2031
1946
451
673
1039
2394
1344
2153
MP-7
1127
2771
1174
1095
2013
736
1465
845
279
799
3841
1564
2542
911
1094
886
408
1346
1294
456
678
1044
2399
1349
2158
MP-8
18188
22845
21530
18465
27594
21532
24683
20316
13086
17645
24038
23446
34117
19465
23157
20495
12142
26491
19485
20094
21366
17735
18580
32259
25795
MP-7
812
945
849
723
1477
2918
846
553
698
716
714
1652
1268
613
424
765
271
697
1034
465
582
1418
774
MP-8
15546
19468
23095
16866
23837
29048
23456
24003
16489
23524
23456
27397
23110
21354
26558
20164
27478
18408
18465
17563
19738
31735
23089
Table VI
Frequency incidence of a MP-g with 450 rotation on LDP for adult facial images.
Adult images
003A49
005A61
006A61
027A41
028A46
033A44
039A52
045A48
047A45
048A50
071A42
062A41
004A53
Sci-img-101
Sci-img-102
Sci-img-103
Sci-img-104
Sci-img-105
mor-img-501
mor-img-502
mor-img-503
mor-img-504
mor-img-505
MP-1
10349
16542
15612
12654
9332
11751
14687
17491
13456
18249
12145
12295
14627
10346
19090
13464
19684
12062
13459
14654
11687
16286
14703
MP-2
864
1165
914
1326
1765
1283
1037
840
1034
483
1348
1660
775
584
1059
1256
1334
581
1360
867
1359
2554
1186
MP-3
649
1021
794
867
1637
1415
913
703
918
490
765
1637
741
596
908
864
1059
571
972
877
1068
2217
1012
MP-4
946
2156
1949
876
2043
1714
2300
1811
785
1624
648
1913
1921
663
1315
1550
1212
1672
1948
975
1356
2278
1296
MP-5
563
2564
1032
946
2516
2157
2013
776
1945
690
1762
2284
1112
703
952
1648
1080
783
1246
1645
1182
2400
1256
153805-7474-IJECS-IJENS © October 2015 IJENS
MP-6
1786
2035
1739
2349
3233
3618
1649
1094
3015
1064
1034
3218
2005
1325
1138
1766
958
1562
2649
2789
1652
2728
1668
IJENS
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
13
Table VII
Frequency incidence of aMP-g with 1350 rotation on LDP for child facial images.
Child images
002A03
008A13
009A03
011A11
012A12
015A01
016A08
019A07
022A11
023A09
073A09
069A03
066A06a
065A09
053A06
Sci-img-01
Sci-img-02
Sci-img-03
Sci-img-04
Sci-img-05
mor-img-01
mor-img-02
mor-img-03
mor-img-04
mor-img-05
MP-1
16908
14038
11282
9605
9022
13230
14458
6239
15688
16120
6154
8419
14332
7009
11791
10395
14656
12349
11256
10356
9468
8864
9862
10354
11349
MP-2
857
1642
1149
665
757
1650
1012
744
1292
1098
1259
1259
1020
639
554
1037
1123
978
981
1035
1210
971
968
811
722
MP-3
758
1104
856
550
651
1475
832
744
894
1201
1315
1315
787
570
529
594
913
795
981
596
637
779
891
730
616
MP-4
940
1144
956
613
693
700
948
1014
873
1175
1058
1058
1017
613
721
894
931
1027
1346
1294
1246
1495
1221
1323
1424
MP-5
889
1129
1061
582
644
2120
1177
1435
900
2164
2254
2254
1146
723
917
1013
1216
1324
1525
1646
2112
2001
1940
899
905
MP-6
1108
1426
1651
962
788
3777
2483
2865
1173
4652
4164
4164
2058
1543
2219
966
2034
2246
3156
4023
3947
3129
798
1039
1164
MP-7
574
595
1056
327
369
2491
2532
2362
652
3839
2819
2819
1140
1367
2208
1345
1649
1864
1447
1552
1336
1649
975
869
713
MP-8
22910
22186
19238
14585
13645
29253
35607
19941
23040
25542
24337
24337
23021
33761
27286
23456
22151
19457
16485
14863
13498
32467
23622
19468
17644
MP-7
775
599
1038
1465
626
1094
1036
669
504
1013
913
1711
1311
2917
745
844
240
966
399
1132
600
948
1414
MP-8
22156
18462
23141
19468
22165
20946
30865
20134
22529
17023
27895
26079
23498
27607
22240
23145
26201
26499
25279
21346
22196
22209
22547
Table VIII
Frequency incidence of a MP-g with 1350 rotation on LDP for adult facial images.
Adult images
003A49
005A61
006A61
027A41
028A46
033A44
039A52
045A48
047A45
048A50
071A42
062A41
004A53
Sci-img-101
Sci-img-102
Sci-img-103
Sci-img-104
Sci-img-105
mor-img-501
mor-img-502
mor-img-503
mor-img-504
mor-img-505
MP-1
13954
10580
9467
8879
14446
9846
15052
1465
16654
10902
12649
11128
10317
10686
17147
13495
18619
10346
17969
8649
13542
13500
8233
MP-2
1178
1353
1532
711
942
917
2492
1265
886
635
937
1824
943
1405
552
1399
1361
1434
1154
1034
1273
808
1795
MP-3
849
1035
741
951
812
756
2354
1094
692
551
963
1655
753
1425
488
794
1052
852
873
355
1012
711
1686
3.1 Comparison of Results with othersProposedmethods
results:
The efficiency of the present method is compared with others
proposed methods like“geometric properties” approach [14]
and SPBPLME[15] methods. Geometric properties approach
MP-4
846
1273
943
694
1933
862
2335
946
1839
1649
864
1946
953
1771
1639
1067
1282
824
1227
761
1317
1991
2114
MP-5
916
1124
761
696
982
851
2356
813
759
763
945
2186
762
2017
633
1034
990
848
935
515
1223
1031
2305
MP-6
2167
1672
2011
1036
1752
853
2800
2034
1082
1474
1943
3201
1864
3750
1070
1846
1005
1765
1182
1946
1595
2035
3420
extracts the geometric facture of Wavelet facial image. The
overall percentage of this method is 91.88%.The proposed
SPBPLMEmethod estimated incidence rates for zero
transitions on prominent binary patterns on SBPLME. The
overall proportion of classification rate of the SPBPLME is
153805-7474-IJECS-IJENS © October 2015 IJENS
IJENS
International Journal of Electrical & Computer Sciences IJECS-IJENS Vol:15 No:05
about 96.13. The proportion of classification in each group of
the proposed method and others methods are listed out in
Table 9. The graphical representation of the classification
results is shown in figure9. The Table 9 clearly indicates that
the proposed method yields better performance rate when
compared with the existing methods.
Table IX
Overall % of classification rates of the proposed method and others methods.
Image
Dataset
FgNet
Morph
Scanned
Google
100.00%
98.00%
96.00%
94.00%
92.00%
90.00%
88.00%
86.00%
84.00%
Geometric
properties
Approach
91.34%
90.79%
89.15%
91.28%
SPBPLME
method
Proposed
Method
96.37%
95.46%
96.13%
96.67%
97.73%
96.96%
98.15%
97.97%
Geometric
properties
Approach
[6]
[7]
[8]
[9]
[ 10 ]
[ 11 ]
[ 12 ]
[ 13 ]
SPBPLME
method
[ 14 ]
Proposed Method
[ 15 ]
14
Zhang S. and Sim T., “Discriminant subspace analysis: a
Fukunaga–Koontz approach,” IEEE Trans. Pattern Anal. Mach.
Intell. 29 (10) (2007) 1732–1745
He X., Yan S., Hu Y., Niyogi P. and Zhang H, “Face recognition
using Laplacianfaces,” IEEE Trans. Pattern Anal. Mach. Intell. 27
(3) (2005) 328–340.
T Ojala, M PietikaÈinen, and D Harwood published a paper entitled
“A Comparative Study of Texture Measures with Classification
Based on Feature Distributions” in the Journal of Pattern
Recognition, Year: 1996 volume: no : 29 Pages 51 to 59.
M Chandra Mohan, V Vijaya Kumar and B Sujatha published a
paper entitled "Classification of child and adult based on geometric
features of face using linear wavelets," in the Journal of Signal and
Image Processing, volume no: 1 and issue no:3, yeAr : 2010
Pages:211 to 220.
"Texton Based Shape Features on Local Binary Pattern for Age
Classification", Chandra Sekhar Reddy et al.; ,IJIGSP 2012, 7, 5460
Novel method of adult age classification using linear wavelet
transforms", Chandra Mohan, VijayaKumar V., Venkata Krishna
V., IJCSNS, pp:1-8, 2010.
Abdel-Mottaleb M. and Elgammal A., “Face Detection in Complex
Environments from Color Images,” IEEE International Conference
Image Processing, pp. 622-626, 1999.
Pantic M. and RothkrantzL.J.M.“Automatic analysis of facial
expressions: the state of the art,” IEEE Trans. Pattern Analysis and
Machine Intelligence, 22(12) (2000) 1424-1445
"Classification of child and adult based on geometric features of
face using linear wavelets", Chandra Mohan M., Vijaya Kumar V.,
Sujatha B., IJSIP, vol.1, Iss.3, pp:211-220, 2010.
“Novel Approach for Child and Adulthood Classification based on
Significant Prominent Binary Patterns of Local Maximum Edge
(SPBPLME)”Rajendra Babu .Ch, DrSreenivasa Reddy. E and
DrPrabhakara Rao. B, I.J. Information Technology and Computer
Science, 2015, 06, 30-37
Fig. 9. Comparison graph of proposed method with others methods
4. CONCLUSIONS
The present paperproposed a novel method for age
classification of child and adults based on the MP-gSon
LDPfacial skin. The novelty of the present method is, it is
rotationally, pose, noise, illumination invariant due to basic
principles of LDP and the proposed MP-g. The present
approach outlines that one need not necessarily evaluate the
frequency of incidence of MP-2g, MP-3g and MP-5g to MP8g for the age classification. The MP-1g and MP-4g contains
more textural and topological information of the facial skin,
that is the reason these two texture features are classifying the
child and adult.
REFERENCE
[1]
[2]
[3]
[4]
[5]
Turk M. and Pentland A. “Eigenfaces for recognition,” Int. J.
Cognitive Neurosci. 3(1) (1991) 71–86.
Turk M. and PentlandA.“Pattern Recognition Learning and
Thought,” pp.486, Pretice Hall, New Jersey, 1973.
Belhumer P. N., Hespanha J.P. and Kriegman D.J. ” “Eigenfaces
vs. Fisherfaces: recognition using class specific linear projection,”
IEEE Trans. Pattern Anal. Mach. Intell. 19 (7) (1997) 711–720.
Yang J. and Yang J.Y. “Why can LDA be performed in PCA
transformed space,” Pattern Recognition 36 (2) (2003) 563–566.
Song F., Zhang D., Mei D. and Guo Z., “A multiple maximum
scatter difference discriminant criterion for facial feature
extraction,” IEEE Trans. Syst. Man Cybern. Part B Cybern. 37 (6)
(2007) 1599–1606.
153805-7474-IJECS-IJENS © October 2015 IJENS
IJENS