International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
Human Age Rank Prediction Using Facial Images
Sarita Ashok Jain#1, Dr.A.J.Patil*2
Lecturer in Electronics Department, Government Polytechnic, JalgaonMaharashtra, India #1
Principle, Shri.GulabraoDeokar College of Engineering, Jalgaon, Maharashtra, India *2
Abstract— A human face provides a lot of information
which allows another person to identify their
characteristics such as age, gender, etc. So the
challenge is to develop an age-group prediction
system by using the machine learning method. The
task of estimating the human's age-group from their
frontal facial images is very captivating, but also the
challenging one due to the personalized and nonlinear pattern of ageing which differs from one person
to another. This work examines the problem of
predicting the age-group of human on the basis of
presenting a facial image with the improved accuracy
of estimation.
Keywords— Age - group prediction ; Support Vector
machine ; K-Nearest Neighbors.
Introduction
There are many different age group estimation
approaches that have been proposed in the past. The
major steps that have been used in the age group
classification areimage pre-processing, feature
extraction, training and testing. So, some of the
methods or algorithms,proposed by the authors, have
been discussedin this paperI. METHODOLOGIES
A. Geometric Features and Wrinkles Feature based
Approaches
Lobo and Kwon proposedthe age determination
algorithm for very first time [1]. Geometric ratioswere
used to make a distinction between babies and other
groups and classified it into three categories: babies,
adult and old. The ratio of the gap between the eyes to
the gap between the eyes and nose is used to
differentiate child from adults. After distinguishing the
babies, skin wrinkles features is measured to
differentiate young from adults and then combine
geometric ratios and skin wrinkles features to find the
age group. But the performance ratewas below 68%
for identifying children and many other issues were
found in their methodeg. Only 47 images were used
for testing and it also requires high resolution images
for prediction of results.
Horng et al. in [2], proposed a model to solve
the wrinkles features and geometric ratiosusing
different approaches. Neural network have been used
to do classification and attained the accuracy of 81.6%
for 230 test images and realized the limitations of
Kwon and Lobo.
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Hayashi et al. in [3], took 300 images ranging
from 14 years to 65 years of age under organized
conditions and used the facial wrinkles analysis
method for age and gender prediction. Histogram
equalization is done onskin section extracted from the
facial images to enhance the wrinkles features and
then to extract shorter and the longer wrinkles DTHT
(Digital Template Hough Transform)is used. Finally,
age and gender are predicted by using a look-up table.
Their experiment was not successful enough as they
have achieved 27% of accuracy on the age prediction
and 83% on gender estimation as the size of their test
dataset has not been defined for their results. Due to
thepresence of makeup,they have faced some
difficulties in extracting the wrinkles on female's age
between 20 and 35.
Lanitis et al. in [4], presented an algorithm
based on the statistical face modelsthat was focused
on the different facial parts for the age estimation. His
work involved the following 4 face parts: whole face
(including hair), internal face (excluding hair), lower
part of face, upper part of the face.Figure1 displays the
used face regions. Experimental results showed that
the region around eyes is most importantfor age
prediction and the upper facial part minimizes the
error. He asserted that hair (when whole face is used)
produces the negative impact on the results. He did
not use the faces with more wrinkles as his work was
limited to only 0 to 35 years of age. Heutilized 80
images for testing purpose out of 330 images.
(a) Whole face (b) internal face (c) eye region (d)
mouth region
Figure1: Different Facial Regions [4]
B. SVM based Approaches
Chen, Yi-Wen, et al. in [5], delivered LucasKanadebased image alignment method and SVM. The
morphology procedure was used first to reduce the
noise in an image and thenthe color region mapping is
applied to obtain human face landmarks and finally,
human face is recognized by employing cascade
method. After detecting the face, an image enhancing
process was used, including face normalization to
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120x100 grayscale image and face illumination that is
remunerated into 128 value image based on equation
(1) and (2) for improving the efficiency of feature
extraction.
=
i
(1)
(2)
Where N is a number of pixels, is an ith image
intensity and
is the average value of intensity. is
the normalized value of intensity.
Lucas-Kanade image alignment method is
taken up for locating52 features points and used these
feature points to construct an active appearance model
(AAM). Then, they warped imagesinto the two
divisions, one for the training set and another for the
test set. After facial image warpingtexture features are
sent to SVM to predict the level of each group.
Finally, average recognition of 81.1% has been
achieved using gray scale value. 87.8% and 82.2% of
recognition ratewas achieved usingSobel detection
method and after combining the gray image with the
edge image respectively.
Tonchev, K., et al. in [6], implemented a
system based on a combination of subspace projection
algorithm and vector classifierfor the age group
estimation and to guarantee the detection of faces, face
detection algorithm is integrated. As shown in
Figure2, their system consists of Face Detection,
Normalization of face, Subspace Projection, Support
Vector Machine for the classification of age.Face
detection includes both Haar-like features and the
Convolution Neural Network (CNN) where CNN role
is to reduce the inferring rate. After detection of face,
in normalization,lighting normalization and geometric
alignmentis done on face. The geometric alignment
guarantees low dependence on the face rotation, while
normalization ensures the better lighting conditions.
For noise reduction, a subspace projection is carried
outin the next phase. It is a combination of Spectral
Regression and Principal Component Analysis(PCA)
algorithms. Finally, SVM classifier is applied for age
group prediction.
enhancement, features are extracted from all
imagesusing the local binary pattern and then passed
to the SVM training the classifier. Somestandard
performance metrics was calculated to test the
algorithms:
1)Mean Absolute Error (MAE) - Absolute mean
difference between real age and predicted ages.
2)Cumulative Score (CS) - the probability such that
age falls within the interval from the real
image.3)Probability Density function (PDF) - to find
the age prediction error.
It has been predicted that total MAE score
using LBP-SVM algorithm is 6.94on real life dataset,
7.29 for MORPH database and 7.47 for FG-NET
database. The algorithm still requires some
improvement,if value of MAE is 4.2, to show the
human comparable results.
Weixing, et al. in [8], used FG-NET database
and self-made face database for the training and
testing. They proposed a method based on
featureextraction by three methods. Firstly, Gabor
wavelet transformis used for extraction of texture
features, i.e. uniform local binary pattern (ULBP).
Figure3 shows the formation process of the
Gabor_ULBP age feature. Secondly, facial partitionis
extracted based on the Active Shape Model (ASM)
approachwhich includes ULBP histogram and
complete partitions of the face.Figure4 shows the
formation process of the ASM_ULBP age feature.
Thirdly, ratio feature, based on facial skin areas and
wrinkles region, is extracted. Figure5 shows facial
skin areas and edge wrinkle detection.The eye position
is labelled usingAdaboost based binocular location
method. Finally, strong SVM classifier is employed
according to abovethree extracted features and their
method reached a recognition rate of 85.75% with the
180 × 180 resolution images.
Figure 2: Age Group Classification System [6]
Khryashchev et al. in [7],presented
experimental results on MORPH, FG-NET and their
own database and predicted the comparison of humans
and machines. They developed a novel algorithm
including adaptive extraction of features using local
binary pattern (LBP) and SVM classification. Firstly,
a color space transformation and scaling process is
used for image preprocessing. Additionally histogram
equalization is performed to improve the image
contrastand unsteady lightening. After the image
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Figure 3: Formation Process of Gabor_ULBP Age
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Feature [8]
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International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
Figure 4: Formation Process of ASM_ULBP Age
Feature [8]
Figure 5: Facial Skin Areas and Edge Wrinkle
positions of the eyes and eye pupil and then histogram
equalization is applied to improve image contrast
occurred due to the unsteady lightening. LBP
histograms produced for each image have been used to
classify the image into one of the predefined classes of
age using K-Nearest Neighbors (KNN) and
obtainedan accuracy of 80%.
Mohammad Ali et al. in[11], proposed a method
based onHistogram of Oriented Gradients (HOG)
feature for age-group estimation. Initially, an image is
cropped to locate the eyes on which the HOG feature
extraction algorithm is applied. This algorithm
provides histogram of oriented gradients in the local
parts of image and features is computed from the
several different regions such as eye-corners,
forehead, near cheekbones and below the eyes. For
each image, these features are concatenated to make a
feature vector and then probabilistic neural network
(PNN) classifier is used to classify the every image in
one of the age groups.Figure7 shows the flow of this
method.
Figure 7: System block diagram based on HOG
Features [11]
Detection [8]
Guo, Guodong, et al. in [9], developed a
Probabilistic Fusion Approach (PFA) thatfuses
regression and classification process for higher age
estimation performance. It is shown in Figure6. They
have chosen Support Vector Regression (SVR)for
regressionandSVM classifier, they work sequentially.
Initially, regression method was used for the
intermediate decision and then these results were sent
to the SVM classifier for the multi-classage
classification.
Izadpanahi et al. in [12], proposed a method
involving geometric analysis of feature and
classification of the age group using three classifiersSVM, neural network (NN) and density based linear
classifier (LDC). Geometric feature initially finds
seventeen facial landmarks and then calculates the six
biometric ratios by using those. Ten facial
measurements as shown in Figure8. After extracting
the biometric ratios, these ratios are used by the
classifiers to categorize the images into five different
age groups,namely AG1 (0-2), AG2 (3-7), AG3 (819), AG4 (20-39), AG5 (40-60). This method
achieved a success rate of 98% in classifying the
babies (0-2) from other age groups.
Figure 6: The PFA approach
C. K-NN and NN based Approaches
Gunay et al. in [10], exercised a method of Local
Binary Pattern (LBP) for the feature extraction and
split the image into n regions to produce LBP
histograms for each image and concatenate all
histograms into a feature vector. Face is detected using
Neural Network initially and calculated the vertical
and horizontal projections of grayscale image to locate
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Figure 8: Ten Facial Measurements with Seventeen
Landmarks [12]
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Ren C. in [13], presented a method that
involves two stages: one was the image preprocessing
and enhancement stage and another is face detection
with the Adaboost algorithm using Haar-like features.
They have simulated their results on the FG-NET and
MORPH database. Images are firstly preprocessed and
converted into grayscale images. Viola Jones method
adapted the use of Haar-like features to detect the face
and eye by cascading a weak classifier. There exist a
large number of Haar-like featureswithin the subwindow of an image. So, this process excludes
complementary features and concerned only about the
critical features. To achieve this goal, they applied
weak learning algorithm proposed by Viola and Jones
that chooses a single feature of the rectangle which
splits the positive samples from the negative samples.
After image preprocessing and feature extraction,
classifier got trained by using the SVM based on
features and then SVM categorized the images in one
of the age groups. They achieved the success rate of
76% in set1 and 93% in set 2 and the average
recognition rate of 84.5%. The subjective
questionnaires are also designed and showed that
people are not proficient of recognizing the human's
age group accurately.
Weixin Li et al.in [14],used a FG - NET
database for their experiment and proposed a novel
algorithm based on Sparse Representation based
Classification (SRC) which chooses human age in a
hierarchical manner. Initially, the image is
preprocessed
by
normalizing.
After
image
normalization,human facial texture featuresare
extracted usingLocal Binary Patterns (LBP) method
and then ASM is used to extract the shape features.
They estimated the age on the basis of two human life
stages: the phase from birth to adult and the phase
from adult to old age. Since, shape of face changes
drastically due to the movements of bones during the
1st stage and vaguely during the 2nd stage. So the
facial features are radically differing between these
two stages. Therefore, in the first step, quadratic
function was used to classify the images into one of
the stages and in the latter step, further preprocessing
of the image was done to solve the age estimation by
employing SRC. SRC required a lot of training
samples of each class which made the efficiency of
age classification limited. So, Ordinal Hyper-planes
Ranker (OHR) was used to improve the results of age
estimation. They evaluated the performance of their
system by using two parameters: Mean Absolute Error
(MAE) and Cumulative Score (CS).
Thukral et al. in [15], implemented the
hierarchical approach where the images of face was
divided into several age groups and separate
regression model was being learned for each age
group. They used FG-NET database as their training
and test set. They used many methods such as
geometric features for extracting thefeatures and
Relevance Vector machine (RVM) as a regression
technique. The main purpose of the RVM is to learn
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the functional relation between the two variables.
During this phase, some training samples are allowed
to overlap so that model can classify the errors that
may occur while allotting the image to one of the age
group. Finally, they classified the test set using
different classifiers such as Nearest Neighbor, μ-SVC,
Partial least squares (PLS) and they used the majority
rule for the classification of test images into the
appropriate age group.
Nithyashri J. et al. in [16], adapted a
techniqueof Wavelet Transformation (WT) for the
extraction of features and Adaptive Resonance Theory
Network (ART) method, as an artificial neural
network, for the classification of age. The Efficiency
of their methodwasobtained usingFG-NET aging
database. Primarily preprocessing was done by
converting input facial color images into gray-scale
images and then features were extracted using
Wavelet Transformation which reduces bits required
of the image. Two types of features have been
extracted in their study including Coif feature and
two-level Haar features. The features like nose, eyes,
chin,
and
mouth
were
extracted.
After
featuresextraction, Euclidean distance was used to
measure the different Feature Point Distances such as
FPD1 is the distance between the eyes, FPD2 is the
distance between the middle of eyes and nose, FPD3is
evaluatedas distance between the eyes and mouth, and
FPD4 is measured as a distance between the mediocre
of eyes and chin. The distance between the (�,) pixel
and (�,) pixel were measured by the formula show in
3. The Adaptive Resonance Theory Network was
trained, based on the FDP, to classify the facial images
into one of the age-groups- child, young, adult and
senior adult.
(3)
Yang, Xi, et al. in [17], proposed Witness
based Multiple Instance Regression (WMIR)algorithm
which modeled the problem of age estimation based
on the framework of Multiple Instance Learning
(MIL) and. The main idea behind WMIR is to find the
positive and negative instances and use both these
instances to train the classifier. Firstly, the image is
preprocessed using Principal Component Analysis
(PCA)for the noise reduction. The probabilistic
weighted Support Vector Regression (pw-SVR)
technique was designed for the estimation of age.
Logistic discriminant metric learning (LDML) was
used for the features space At last,pw-SVM was
trained using these feature spaces to categorize the age
accordingly.
D. Active Appearance based Approaches
Liu, Li in [18], put up an approach in which age
was categorized into five different age-groups.
Initially, all the images were converted into grayscale
and then preprocessed using the image intensity
normalization and implemented by histogram
equalization. The Active Appearance Model has been
utilized for the featureextractionthen these features
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could be used to train the classifier with the Gaussian
Radian Basis Function kernel (RBF). Since, feature
space was so large, they used Principle Component
Analysis (PCA) to reduce the length of feature space.
At last, test set was sent to the Support Vector
Machine classifier to categorize the age into one of the
five groups based on the training dataset.
Lee, Seung Ho et al. in [19], classified the age
based on local age group modeling, which is erected
by clustering trained faces. This method also helped in
dealing with large variations of facial appearance.
Whole training images of an age group were
decomposed into a different set of face clusters to
avoid the degradation of classification due to some
disagreeable faces. They provided the effective way of
computing the distance between the centroid of face
cluster and the test face and way of computing
distance between training face sample distribution
formed and the test face. These measures were used to
improve the distinction between the clusters of
different age groups. Since the face clusters may differ
with the different database, hierarchical method of
clustering seems more suitable than the k-means
clustering. TheyextractedLBP histogram features and
then classified the age group based on hierarchical
clustering and achieved an estimation rate of 60%.
Ueki, Kazuya et al. in [20], presented a framework
for the age-group classification under different
lighting conditions. Their method was based on the
combination oftwo-phased approaches named 2D
LDA and LDA and the results of the experiment
showed that their approach improved the accuracy of
classification more than the LDA approach and PCAbased approach. PCA is an approach which calculates
a vector that has the biggest variable in the training
data and LDA is a method that discovers a projection
for maximizing the ratio between the class scatter and
within the class and features can also be extracted
using LDA. Sometimes Small Sample Size Problem
(S3 problem) may be encountered due to the high
dimensionality of facial images. To overcome this
problem, PCA was used for the reduction of
dimensionality. The Gaussian model classifier was
trained using a training-set images based on LDA and
used to classify the age-groups for the test set. They
used a WIT database for the various lighting images
and attained the accuracy rate of 46.3% for 5-year
range, 76.8%for 10-year range and 78.1% for 15-year
range age-group.
Chao et al. in [21], proposed an approach of age
estimation with the three narrative contributions.
Firstly, the relation between the age labels and facial
features was discovered based on dimensionality
reduction (reduce the feature size) and distance
learning metric. Then the intrinsic ordinal relationship
has been exploited among age based on label sensitive
conceptto solve the disparity of age classes. At last,to
confine the human ageing complex nature the local
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regression technique is exploited. For the adjustment
of distance metric,they suggested a Label-sensitive
Locality Preserving Projections (LsLPP) Labelsensitive Relevant Component Analysis (LsRCA)
using AAM. They used a FG - NET database to get
experimental results. TheycombinedSupport Vector
Machine (SVM) and K-Nearest Neighbors (KNN) to
generate excellent results with Mean Absolute Error
(MAE) of 4.38.
II. CONCLUSIONS
In this paper different approaches for age rank
estimation are studied. Most research in the area of
age prediction is limited by the good choice of
database used and the size of the database. Some
researchers have only focused on the certain age
groups, while some have employed the wide range of
classification. Due to the lack of quality database, a
universal age prediction function for the wide range of
ages is yet to be developed. From the previous work, it
can be concluded that the region around the eyes was
most crucial for age prediction and hair produces the
negative impact on the results.
ACKNOWLEDGMENT
The authors are grateful to all the people around them
for their help and support to improve thispaper.
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