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AGE ESTIMATION: A CLASSIFICATION PROBLEM. HANDE ALEMDAR, BERNA ALTINEL, NEŞE ALYÜZ, SERHAN DANİŞ. Project Overview. Subset Overview. Aging Subset of Bosphorus Database: 1-4 neutral and frontal 2D images of subjects 105 subjects Total of 298 scans Age range: [18-60]

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age estimation a classification problem

AGE ESTIMATION: A CLASSIFICATION PROBLEM

HANDE ALEMDAR, BERNA ALTINEL, NEŞE ALYÜZ, SERHAN DANİŞ

subset overview
Subset Overview
  • Aging Subset of Bosphorus Database:
    • 1-4 neutral and frontal 2D images of subjects
    • 105 subjects
    • Total of 298 scans
    • Age range: [18-60]
    • Age distribution non uniform: average = 29.9
project overview1
Project Overview
  • Aging images of individuals is not present
  • Aim: Age Estimation based on Age Classes
  • 3 Classes:
      • Age<26 -> 96 samples
      • 26 <= Age <= 35 -> 161 samples
      • Age>36 -> 41 samples
preprocessing
Preprocessing
  • Registration
  • Cropping
  • Histogram Equalization
  • Resizing
age manifold
Age Manifold
  • Instead of learning a subject-specific aging pattern, a common aging trend can be learned
  • Manifold embedding technique to learn the low-dimensional aging trend.

Image space:

Labels:

Low-dim. representation:

d<<D

Mapping:

orthogonal locality preserving projections olpp
Orthogonal Locality Preserving Projections - OLPP
  • Subspace learning technique
  • Produces orthogonal basis functions on LPP
  • LPP:

The essential manifold structure preserved by measuring local neighborhood distances

  • OLPP vs. PCA for age manifold:
      • OLPP is supervised, PCA is unsupervised
      • OLPP better, since age labeling is used for learning

X Size of training data for OLPP should be LARGE enough

locality preserving projection lpp
Locality Preserving Projection - LPP
  • aka: Laplacianface Approach
  • Linear dimensionality reduction algorithm
  • Builds a graph:

based on neighborhood information

  • Obtains a linear transformation:

Neighborhood information is preserved

slide10
LPP
  • S: similarity matrix defined on data points (weights)
  • L = D – S : graph Laplacian
  • D: diagonal sum matrix of S

measures local density around a sample point

  • Minimization problem:

with the constraint :

=> Minimizing this function: ensure that if xi and xj are close then their projections yi and yj are also close

slide11
LPP
  • Generalized eigenvalue problem:
  • Basis functions are the eigenvectors of:

Not symmetric, therefore the basis functions are not orthogonal

slide12
OLPP
  • In LPP, basis functions are nonorthogonal
    • > reconstruction is difficult
  • OLPP produces orthogonal basis functions
    • > has more locality preserving power
olpp algorithmic outline
OLPP – Algorithmic Outline

(1) Preprocessing: PCA projection

(2) Constructing the Adjacency Graph

(3) Choosing the Locality Weights

(4) Computing the Orthogonal Basis Functions

(5) OLPP Embedding

1 preprocessing pca prjection
(1) Preprocessing: PCA Prjection
  • XDXT can be singular
  • To overcome the singularity problem -> PCA
  • Throwing away components, whose corresponding eigenvalues are zero.
  • Transformation matrix: WPCA
  • Extracted features become statistically uncorrelated
2 constructing the adjacency graph
(2) Constructing The Adjacency Graph
  • G: a graph with n nodes
  • If face images xi and xj are connected (has the same label) then an edge exists in-between.
3 choosing the locality weights
(3) Choosing the Locality Weights
  • S: weight matrix
  • If node i and j are connected:
  • Weights: heat kernel function
  • Models the local structure of the manifold
4 computing the orthogonal basis functions
(4) Computing the Orthogonal Basis Functions
  • D: diagonal matrix, column sum of S
  • L : laplacian matrix, L = D – S
  • Orthogonal basis vectors:
  • Two extra matrices defined:
  • Computing the basis vectors:
    • Compute a1 : eigenvector of with the greatest eigenvalue
    • Compute ak : eigenvector of

with the greatest eigenvalue

5 olpp embedding
(5) OLPP Embedding
  • Let:
  • Overall embedding:
subspace methods pca vs olpp
Subspace Methods: PCA vs. OLPP
  • Face Recognition Results on ORL
subspace methods pca vs olpp1
Subspace Methods: PCA vs. OLPP
  • Face Recognition Results on Aging Subset of the Bosphorus Database
  • Age Estimation (Classification) Results on Aging Subset of the Bosphorus Database
feature extraction
Feature Extraction
  • LBP - Local Binary Patterns
local binary patterns
Local Binary Patterns
  • More formally
  • For 3x3 neighborhood we have 256 patterns
  • Feature vector size = 256

where

uniform lbp
Uniform LBP
  • Uniform patternscan be used to reduce the length of the feature vector and implement a simple rotation-invariant descriptor
  • If the binary pattern contains at most two bitwise transitions from 0 to 1 or vice versa when the bit pattern is traversed circularly Uniform
    • 01110000 is uniform
    • 00111000 (2 transitions)
    • 00011100 (2 transitions)
  • For 3x3 neighborhood we have 58 uniform patterns
  • Feature vector size = 59
slide26

Gabor Filter

  • Band-pass filters used for feature extraction, texture analysis and stereo disparity estimation.
  • Can be designed for a number of dilations and rotations.
  • The filters with various dilations and rotations are convolved with the signal, resulting in a so-called Gabor space. This process is closely related to processes in the primary visual cortex.
gabor filter

A set of Gabor filters with different frequencies and orientations may be helpful for extracting useful features from an image.

We used 6 different rotations and 4 different scales on 16 overlapping patches of the images.

We generate 768 features for each image.

Gabor Filter
slide29
EXPERIMENTAL DATASETS1. Features_50_45(LBP) 2. Features_100_90(LBP)3. Features_ORIg(LBP)4. Features_50_45(GABOR)5. Features_100_90 (GABOR)
slide30

Experiment #1

Estimate age, just based on the average value of the training set

k nearest neighbor algorithm

The K-nearest-neighbor (KNN) algorithm measures the distance between a query scenario and a set of scenarios in the data set.

Experiments #2

K-nearest-neighbor algorithm
in progress

[2

[2

1. Parametric Classification

2. Mahalanobis distance can be used as the distance measure in kNN.

IN PROGRESS:
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