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### AGE ESTIMATION: A CLASSIFICATION PROBLEM

HANDE ALEMDAR, BERNA ALTINEL, NEŞE ALYÜZ, SERHAN DANİŞ

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 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

- Registration
- Cropping
- Histogram Equalization
- Resizing

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

- 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

- aka: Laplacianface Approach
- Linear dimensionality reduction algorithm
- Builds a graph:

based on neighborhood information

- Obtains a linear transformation:

Neighborhood information is preserved

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

LPP

- Generalized eigenvalue problem:
- Basis functions are the eigenvectors of:

Not symmetric, therefore the basis functions are not orthogonal

OLPP

- In LPP, basis functions are nonorthogonal
- > reconstruction is difficult
- OLPP produces orthogonal basis functions
- > has more locality preserving power

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

- 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

- 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

- 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

- 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

- Let:
- Overall embedding:

Subspace Methods: PCA vs. OLPP

- Face Recognition Results on ORL

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

- LBP - Local Binary Patterns

Local Binary Patterns

- More formally
- For 3x3 neighborhood we have 256 patterns
- Feature vector size = 256

where

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

- 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.

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 FilterEXPERIMENTAL DATASETS1. Features_50_45(LBP) 2. Features_100_90(LBP)3. Features_ORIg(LBP)4. Features_50_45(GABOR)5. Features_100_90 (GABOR)

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

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[2

1. Parametric Classification

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

IN PROGRESS:1. Other distance functions can be analyzed for kNN:

2. Normalization can be applied:

POSSIBLE FUTURE WORK ITEMS:
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