AGE ESTIMATION: A CLASSIFICATION PROBLEM

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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

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

(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
• 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

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.

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
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)

Experiment #1

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

[2

1. Parametric Classification

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

IN PROGRESS: