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Three different classifiers for facial age estimation based on K-nearest neighbor
 

Three different classifiers for facial age estimation based on K-nearest neighbor

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Abstract - The exact age estimation is often treated as a ...

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 classifiers for facial age estimation based on K-nearest neighbor Three different classifiers for facial age estimation based on K-nearest neighbor Presentation Transcript

    • THREE DIFFERENT CLASSIFIERS FOR FACIAL AGE ESTIMATION BASED ON K-NEAREST NEIGHBOR By Alaa Tharwat Electrical Engineering Department, Suez Canal University, Fac. of Eng. Ismailia, EGYPT ICENCO 28-29/12/2013 – Cairo Egypt
    • Scientific Research Group in Egypt www.egyptscience.net
    • Agenda Introduction Proposed Method General framework Feature extraction and fusion Three Classification Experimental Results Conclusions ICENCO 28-29/12/2013 – Cairo Egypt 3
    • Introduction • • Age estimation is the determination of a person’s age based on biometric features (2D Face image). Facial aging effects are mainly attributed to: • • • Bone growth Skin related deformations associated with the introduction of wrinkles (texture changes) Muscle strength
    • Introduction Background of Facial Age Estimation     Used FGNET, Morph, Own database Applications • Age-Based Access Control • Conventional classification and feature extraction methods. Age Adaptive Human Machine Interaction (HCI) • Used Local, Global , or feature fusion method. Age Invariant Person Identification • Data mining and organization Classification or Regression.
    • Introduction Challenges • • • • • Different expressions Inter-person variation Lighting variation Face orientation Occlusions  Moreover, age estimation from 2D face images has the following challenges • Limited inter-age group variation • Diversity of aging variation • Dependence on external factors • Data availability
    • The proposed age estimation approach: General framework
    • The proposed age estimation approach: Feature extraction and fusion Local binary pattern (LBP) Features Sub - Window image 150 120 160 152 150 60 40 20 22 20 35 30 30 33 30 30 35 37 30 35 40 43 45 40 37 70 60 50 45 40 Thresholding 150 120 160 1 1 1 60 40 20 1 135 0 35 30 30 0 0 LBP Code (10000111)2 =135 0 Illustration of LBP. Typically the binary codes obtained by local thresholding are transformed into decimal codes.
    • The proposed age estimation approach: Feature extraction and fusion Landmarks (Fiducial) Points Some images of the FG-NET database with landmarks
    • The proposed age estimation approach: Feature extraction and fusion Feature fusion Advantage the fusion in feature level contains richer information than classification level Disadvantage • The features may be incompatible, so it needs to normalization. • The new feature vector needs more CPU time and memory (Dimensionality problem), so it needs to dimensionality reduction techniques. Local features (f1=[l1,…….,lm]) Normalization (f’1) Global features (f2=[g1,……..,gn]) Normalization (f’2) New Feature vector fnew =[f’1 f’2] =[l1,…….,lm,g1,……..,gn]
    • The proposed age estimation approach: Three Classification  The first classifier   The second classifier   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. A modified-KNN version was proposed and the classifier scoring results interpolated to calculate the exact age estimation. The third classifier  KNN-regression classifier as third classifier that used to combine the classification and regression approaches to improve the accuracy of 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 618 face images at different ages. The ages are distributed in a wide range from 0 to 69. Besides age variation, most of the age-progressive image sequences display other types of facial variations, such as significant 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 AGE ESTIMATION ON FGNET 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, ModifiedKNN, and KNN-Regression) gives good age estimation process and estimating age when gender is known Estimating age from males achieves results better than females.
    • Questions