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Abstract - The exact age estimation is often treated as a classification problem; while it can be formulated as a regression problem. In this article, three different classifiers based on KNN classifier's concept for facial age estimation were designed and developed to achieve high efficiency calculation of facial age estimation. In the first classifier, we adopt KNN-distance approach to calculate minimum distance between test face image and all instances belong to the class that has the highest number of nearest samples. Additionally, in the second classifier a modified-KNN version was proposed and the classifier scoring results interpolated to calculate the exact age estimation. Furthermore, KNN-regression classifier as third classifier that used to combine the classification and regression approaches to improve the accuracy of the age estimation system. Moreover, we compared age estimation errors under two situations: case 1, age estimation is performed without discrimination between males and females (gender unknown); and case 2, age estimation is performed for males and females separately (gender known). Results of experiments conducted on well know benchmark FG-NET Database show the effectiveness of the proposed approach.
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THREE DIFFERENT CLASSIFIERSFOR FACIAL AGE ESTIMATION BASED
ON K-NEAREST NEIGHBOR
ByAlaa Tharwat
Electrical Engineering Department, Suez Canal University,Fac. of Eng. Ismailia, EGYPT
ICENCO 28-29/12/2013 – Cairo Egypt
Scientific Research Group in Egyptwww.egyptscience.net
IntroductionProposed Method
General frameworkFeature extraction and fusionThree Classification
Experimental ResultsConclusions
AgendaIntroductionProposed Method
General frameworkFeature extraction and fusionThree Classification
Experimental ResultsConclusions
3ICENCO 28-29/12/2013 – Cairo Egypt
Introduction• Age estimation is the determination of
a person’s age based on biometricfeatures (2D Face image).
• Facial aging effects are mainlyattributed to:• Bone growth• Skin related deformations associated with
the introduction of wrinkles (texturechanges)
• Muscle strength
• Age estimation is the determination ofa person’s age based on biometricfeatures (2D Face image).
• Facial aging effects are mainlyattributed to:• Bone growth• Skin related deformations associated with
the introduction of wrinkles (texturechanges)
• Muscle strength
Introduction
Used FGNET, Morph, Owndatabase
Conventional classification andfeature extraction methods.
Used Local, Global , or featurefusion method.
Classification or Regression.
• Age-Based Access Control• Age Adaptive Human Machine
Interaction (HCI)• Age Invariant Person
Identification• Data mining and organization
Background of Facial AgeEstimation Applications
Used FGNET, Morph, Owndatabase
Conventional classification andfeature extraction methods.
Used Local, Global , or featurefusion method.
Classification or Regression.
• Age-Based Access Control• Age Adaptive Human Machine
Interaction (HCI)• Age Invariant Person
Identification• Data mining and organization
Introduction
• Different expressions• Inter-person variation• Lighting variation• Face orientation• Occlusions
Moreover, age estimation from2D face images has the followingchallenges• Limited inter-age group variation• Diversity of aging variation• Dependence on external factors• Data availability
Challenges
• Different expressions• Inter-person variation• Lighting variation• Face orientation• Occlusions
Moreover, age estimation from2D face images has the followingchallenges• Limited inter-age group variation• Diversity of aging variation• Dependence on external factors• Data availability
The proposed age estimationapproach: General framework
The proposed age estimationapproach: Feature extraction and fusion
Local binary pattern (LBP) Features
150152160120150
2022204060160120150
204060
11101351
Sub - Window Thresholding LBP Code(10000111)2=135
image
2022204060
3033303035
3530373530
3740454340
4045506070
204060
303035
01351000
(10000111)2=135
Illustration of LBP. Typically the binary codesobtained by local thresholding are transformed into
decimal codes.
The proposed age estimationapproach: Feature extraction and fusion
Landmarks (Fiducial) Points
Some images of the FG-NET database withlandmarks
The proposed age estimationapproach: Feature extraction and fusion
Feature fusionAdvantagethe fusion in feature level contains richer information than classification levelDisadvantage• The features may be incompatible, so it needs to normalization.• The new feature vector needs more CPU time and memory (Dimensionalityproblem), so it needs to dimensionality reduction techniques.
Disadvantage• The features may be incompatible, so it needs to normalization.• The new feature vector needs more CPU time and memory (Dimensionalityproblem), so it needs to dimensionality reduction techniques.
Local features (f1=[l1,…….,lm])
Global features (f2=[g1,……..,gn])
Normalization(f’1)
Normalization(f’2)
New Feature vectorfnew =[f’1 f’2]
=[l1,…….,lm,g1,……..,gn]
The proposed age estimationapproach: Three Classification The first classifier
KNN-distance approach to calculate minimumdistance between test face image and allinstances belong to the class that has thehighest number of nearest samples.
The second classifier A modified-KNN version was proposed and the
classifier scoring results interpolated to calculatethe exact age estimation.
The third classifier KNN-regression classifier as third classifier that
used to combine the classification andregression approaches to improve the accuracyof the age estimation system
The first classifier KNN-distance approach to calculate minimum
distance between test face image and allinstances belong to the class that has thehighest number of nearest samples.
The second classifier A modified-KNN version was proposed and the
classifier scoring results interpolated to calculatethe exact age estimation.
The third classifier KNN-regression classifier as third classifier that
used to combine the classification andregression approaches to improve the accuracyof the age estimation system
Experimental Results
[14] http://www.fgnet.rsunit.com/.
The FG-NET Aging Database [*] is used in the experiment. There are 1,002 face images from 82 subjects in this database. Each subject has 6-18 face images at different ages. The ages are distributed in a widerange from 0 to 69. Besides age variation, most of the age-progressiveimage sequences display other types of facial variations, such assignificant changes in 3D pose, illumination, expression, etc.
Experimental Results
The age range distribution of the images in the FG-NET Database
Experimental Results
MAES OF AGEESTIMATION ON FG-
NET DATABASE
MAES OF AGEESTIMATION ON FG-
NET DATABASE
Experimental Results
MAES OF AGE ESTIMATION ON FG-NET DATABASE
Conclusions Proposed classifiers achieved relatively good age
estimation from 2D face images Proposed age estimation system based on three
proposed classifiers (KNN-Distance, Modified-KNN, and KNN-Regression) gives good ageestimation process and estimating age when genderis known
Estimating age from males achieves results betterthan females.
Proposed classifiers achieved relatively good ageestimation from 2D face images
Proposed age estimation system based on threeproposed classifiers (KNN-Distance, Modified-KNN, and KNN-Regression) gives good ageestimation process and estimating age when genderis known
Estimating age from males achieves results betterthan females.
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