a comparative study of pca and lda for face recognition
TRANSCRIPT
![Page 1: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/1.jpg)
![Page 2: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/2.jpg)
Introduction Face recognition Problem Face detection method
› PCA› FLD› Face detection step› PCA vs LDA
Experiment and result Conclusion Future work
![Page 3: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/3.jpg)
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
![Page 4: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/4.jpg)
These methods are most widely used and have relative simple processing steps that could be advantageous when it is applied to embedded systems
![Page 5: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/5.jpg)
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.
![Page 6: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/6.jpg)
Principle Component Analysis (PCA) Linear Discriminant Analysis (LDA)
![Page 7: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/7.jpg)
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
![Page 8: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/8.jpg)
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
![Page 9: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/9.jpg)
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.
![Page 10: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/10.jpg)
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
![Page 11: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/11.jpg)
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
![Page 12: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/12.jpg)
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
![Page 13: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/13.jpg)
PCA LDA
optimal discrimination
Covariance computation
ST=SB+SW
The eigenvectors calculation
W=eig( ST) W=eig( SW-1SB)
Tii
c
iT xxS ))((
1
c
i x
TikikW
ik
xS1
))((
Tii
c
iiBS ))((
1
WSW
WSWW
WT
TT
optW
max argWSW
WSWW
WT
BT
optW
max arg
![Page 14: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/14.jpg)
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.
![Page 15: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/15.jpg)
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
![Page 16: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/16.jpg)
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.
![Page 17: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/17.jpg)
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
![Page 18: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/18.jpg)
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
![Page 19: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/19.jpg)
![Page 20: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/20.jpg)
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
![Page 21: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/21.jpg)
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
![Page 22: A Comparative Study of PCA and LDA for Face Recognition](https://reader033.vdocuments.mx/reader033/viewer/2022061200/5474f54cb4af9f97668b4577/html5/thumbnails/22.jpg)
Thank You.