Introduction Face recognition Problem Face detection method

› PCA› FLD› Face detection step› PCA vs LDA

Experiment and result Conclusion Future work

Face recognition has recently received significant attention, especially during the past several years

Face recognition is one method of identity authentication on biometrics study.

Face recognition has several characteristics that are advantageous for consumer

Wide range of applications

These methods are most widely used and have relative simple processing steps that could be advantageous when it is applied to embedded systems  

The challenges of face recognition are the rapid and accurate identification or classification of a query image.

Rapid can be associated to speed, and accuracy actually refers to recognition rate.

Principle Component Analysis (PCA) Linear Discriminant Analysis (LDA)

PCA= The Hotelling= Eigenspace Projection= Karhunen and Leove (KL) transform

Eigenfaces technique : implementation of the PCA method in face recognition

PCA has become one of the most successful approaches in face recognition

It was presented by Turk and Pentland in 1991

They projecting Face images onto a face space defined by the ‘eigenfaces’ and the eigenvectors of the set of faces not necessary corresponded to isolated features (eyes, ears, and noses).

They use PCA for dimensionality reduction in order to find the best account of vectors for the distribution of face images within the entire image space

Sensitive to illumination and expression,

Difficult to evaluate the covariance matrix accurately.

Could not capture even the simplest invariance unless this information is explicitly provided in the training data.

LDA or Fisher’s Linear Discriminant (FLD) is a dimensionality reduction technique which is used for classification problems.

This approach tries to find the projection direction in which, images belonging to different classes are separated maximally

The within-class scatter matrix (intra-personal), represents variations in appearance of the same individual due to different lighting and face expression.

The between-class scatter matrix (extra-personal), represents variations in appearance due to a difference in identity

Training Phase› The image samples are taken as grayscale images.› Transformed from 2D matrix to 1D column vector› Centered the vector of data matrix X with subtracting

by the mean vector› Compute the covariance matrix of the column vectors› Compute the eigenvalues and corresponding

eigenvectors› Projecting the data matrix X onto the eigenspace,

Recognize Phase› Convert the image I that will be recognized into 1D

vector, and subtracting by mean.› Projecting on to same eigenspace.› Compute the Euclidean distance› Choose the minimum Euclidean distance value

PCA LDA

optimal discrimination

Covariance computation

ST=SB+SW

The eigenvectors calculation

W=eig( ST) W=eig( SW-1SB)

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Tii

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WSW

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WT

TT

optW

max argWSW

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Each algorithm has been generated using Matlab.

Sample training and test databases are parts of 'face94' Essex face database› 10 faces image,› each face image has three different

variations› the total number is 30 sample images. › RGB› 180x200 pixels.

Experiment was conducted for five times. › First : used 5 images in both test and

training database.› Second : used 10 images in both test and training

database. › Third : used 10 images in test database and

20 images in training database. › Fourth : used 20 images in test database and

10 images in training database. › Fifth : used both 20 images in test and

training databases

The speed or time taken measures the process of covariance computation until the minimum Euclidian distance yielded.

While the recognition rate shows the accuracy of the methods.

Method  

Test and Training Set5 10 20

5 10 20 10 20PCA 0.6 0.9 1 0.8 1LDA 0.6 0.8 0.9 0.5 0.95

Method  

Test and Training Set5 10 20

5 10 20 10 20PCA 0.074293 0.13841 0.37788 0.040033 0.341087LDA 0.080923 0.15225 0.31275 0.035142 0.260113

A comparative performance analysis of PCA and LDA was conducted on face recognition using different number of training and test images in five experiments

Performance evaluations cover the speed and recognition rate for both methods.

The results of the experiments show that PCA is better than LDA in recognition rate.

In term of speed, PCA tend to be much better then LDA for few sample of images.

However, for the larger number of sample use LDA outperforms to PCA

The future works can be conducted by combining PCA and LDA.

The idea of applying PCA and LDA is based on the fact that combined both methods may compensate for the drawbacks of each other

Thank You.