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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

• 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

• 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

• Registration

• Cropping

• Histogram Equalization

• Resizing

Neşe Alyüz

Subspace analysis for age estimation

• 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:

• 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

• aka: Laplacianface Approach

• Linear dimensionality reduction algorithm

• Builds a graph:

based on neighborhood information

• Obtains a linear transformation:

Neighborhood information is preserved

• 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

• Generalized eigenvalue problem:

• Basis functions are the eigenvectors of:

Not symmetric, therefore the basis functions are not orthogonal

• In LPP, basis functions are nonorthogonal

• > reconstruction is difficult

• OLPP produces orthogonal basis functions

• > has more locality preserving power

(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.

• S: weight matrix

• If node i and j are connected:

• Weights: heat kernel function

• Models the local structure of the manifold

• 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

• Let:

• Overall embedding:

• Face Recognition Results on ORL

• Face Recognition Results on Aging Subset of the Bosphorus Database

• Age Estimation (Classification) Results on Aging Subset of the Bosphorus Database

Feature extraction: Local binary patterns

• LBP - Local Binary Patterns

• More formally

• For 3x3 neighborhood we have 256 patterns

• Feature vector size = 256

where

• 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

Serhan Daniş

Feature extraction: Gabor Filtering

• 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

Berna orientations may be helpful for extracting useful features from an image. Altınel

Classification

EXPERIMENTAL DATASETS orientations may be helpful for extracting useful features from an image.

1. 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 orientations may be helpful for extracting useful features from an image.

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

EXPERIMENTAL RESULTS: orientations may be helpful for extracting useful features from an image.

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

EXPERIMENTAL RESULTS: between a query scenario and a set of scenarios in the data set.

[ between a query scenario and a set of scenarios in the data set. 2

[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: between a query scenario and a set of scenarios in the data set.

2. Normalization can be applied:

POSSIBLE FUTURE WORK ITEMS: