AGE ESTIMATION: A CLASSIFICATION PROBLEM

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]– 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

SUBSPACE ANALYSIS FOR AGE ESTIMATION

Neşe Alyüz

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 learningX 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: LOCAL BINARY PATTERNS

Hande Alemdar

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 patterns can 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

FEATURE EXTRACTION: GABOR FILTERING

Serhan Daniş

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.

CLASSIFICATIONBerna Altınel

EXPERIMENTAL DATASETS

1. 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

Experiment #1

EXPERIMENTAL RESULTS:INPUT METHOD Mean(MRE) % Number of

Correct classifications

Number of missclassifications

Features_50_45-LBP

Estimating the Average Age 17.31 162 / 298 135 / 298

Features_100_90-LBPFeatures_orig-LBP

Features_50_45(GABOR)Features_100_90(GABOR)

K-NEAREST-NEİGHBOR ALGORİTHM

Experiments #2

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

EXPERIMENTAL RESULTS:

INPUT Features_50_45(LBP)

Features_100_90(LBP)

Features_orig(LBP)

Features_50_45(GABOR)

Features_100_90(GABOR)METHOD

kNN-1(Euc Dist)

MRE(%):5.05 MRE(%):4.14 MRE(%):11.11

MRE(%):3.88 MRE(%):3.75

kNN-2(Euc Dist)

MRE(%):6.77 MRE(%):5.17 MRE(%):11.97

MRE(%):4.92 MRE(%):5.08

kNN-3(Euc Dist)

MRE(%):7.50 MRE(%):6.06 MRE(%):12.50

MRE(%):5.79 MRE(%):5.96

kNN-5(Euc Dist)

MRE(%):10.40

MRE(%):9.36 MRE(%):13.15

MRE(%):11.02

MRE(%):10.93

kNN-10(Euc Dist)

MRE(%):12.34

MRE(%):11.57

MRE(%):14.16

MRE(%):13.86

MRE(%):14.13

kNN-15(Euc Dist)

MRE(%):12.85

MRE(%):12.30

MRE(%):14.35

MRE(%):14.57

MRE(%):14.98

Features_50_45(LBP)

Features_100_90(LBP)

Features_orig(LBP)



METHOD

Average MRE(%):15.72

MRE(%):15.01

MRE(%):15.58

MRE(%):16.31

MRE(%):16.55

MissClass:35 / 298

MissClass:32 / 298

MissClass:31 / 298

MissClass:32 / 298

MissClass:27 / 298

CorrectClass:262 / 298





IN PROGRESS:1. Parametric Classification

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

[2 [2

1. Other distance functions can be analyzed for kNN:

2. Normalization can be applied:

POSSIBLE FUTURE WORK ITEMS:

AGE ESTIMATION: A CLASSIFICATION PROBLEM

Documents

Transcript of AGE ESTIMATION: A CLASSIFICATION PROBLEM