1 / 19

Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces)

Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces). Pradeep Buddharaju. COSC 6397. U of H. Principal Component Analysis. A N x N pixel image of a face, represented as a vector occupies a single point in N 2 -dimensional image space.

grizelda
Télécharger la présentation

Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces) Pradeep Buddharaju COSC 6397 U of H

  2. Principal Component Analysis • A N x N pixel image of a face, represented as a vector occupies a single point in N2-dimensional image space. • Images of faces being similar in overall configuration, will not be randomly distributed in this huge image space. • Therefore, they can be described by a low dimensional subspace. • Main idea of PCA (cutler96): • To find vectors that best account for variation of face images in entire image space. • These vectors are called eigen vectors. • Construct a face space and project the images into this face space (eigenfaces).

  3. Image Representation • Training set of images of size N*N are represented by vectors of size N2 1,2,3,…,M Example

  4. Average Image and Difference Images • The average training set is defined by Ψ = (1/M) ∑Mi=1i • Each face differs from the average by vector Φi = Γi – Ψ

  5. Covariance Matrix • A covariance matrix is constructed as: C = AAT, where A=[Φ1,…,ΦM] of size N2x N2 • Finding eigenvectors of N2x N2 matrix is intractable. Hence, use the matrix ATA of size M x M and find eigenvectors of this small matrix. Size of this matrix is N2 x N2 Size of this matrix is M*M

  6. Eigenvalues and Eigenvectors - Definition • If v is a nonzero vector and λ is a number such that Av = λv, then              v is said to be an eigenvector of A with eigenvalue λ. Example l (eigenvalues) (eigenvectors) A v

  7. How to Calculate Eigenvectors?

  8. Eigenvectors of Covariance Matrix • The eigenvectors vi of ATA are: • Consider the eigenvectors vi of ATA such that • ATAvi = ivi • Premultiplying both sides by A, we have • AAT(Avi) = i(Avi)

  9. Face Space • The eigenvectors of covariance matrix are ui = Avi Face Space • ui resemble facial images which look ghostly, hence called eigenfaces

  10. Projection into Face Space • A face image can be projected into this face space by Ωk = UT(Γk – Ψ); k=1,…,M Projection of Image1

  11. Recognition • The test image, Γ, is projected into the face space to obtain a vector, Ω: Ω = UT(Γ – Ψ) • The distance of Ω to each face class is defined by Єk2 = ||Ω-Ωk||2; k = 1,…,M • A distance threshold,Өc, is half the largest distance between any two face images: Өc = ½ maxj,k {||Ωj-Ωk||}; j,k = 1,…,M

  12. Recognition • Find the distance, Є , between the original image, Γ, and its reconstructed image from the eigenface space, Γf, Є2 = || Γ – Γf ||2 , where Γf = U * Ω + Ψ • Recognition process: • IF Є≥Өcthen input image is not a face image; • IF Є<ӨcAND Єk≥Өc for all k then input image contains an unknown face; • IF Є<Өc AND Єk*=mink{ Єk} < Өcthen input image contains the face of individual k*

  13. Limitations of Eigenfaces Approach • Variations in lighting conditions • Different lighting conditions for enrolment and query. • Bright light causing image saturation. • Differences in pose – Head orientation • - 2D feature distances appear to distort. • Expression • - Change in feature location and shape.

  14. Linear Discriminant Analysis • PCA does not use class information • PCA projections are optimal for reconstruction from a low dimensional basis, they may not be optimal from a discrimination standpoint. • LDA is an enhancement to PCA • Constructs a discriminant subspace that minimizes the scatter between images of same class and maximizes the scatter between different class images

  15. Mean Images • Let X1, X2,…, Xc be the face classes in the database and let each face class Xi, i = 1,2,…,c has k facial images xj, j=1,2,…,k. • We compute the mean image i of each class Xi as: • Now, the mean image  of all the classes in the database can be calculated as:

  16. Scatter Matrices • We calculate within-class scatter matrix as: • We calculate the between-class scatter matrix as:

  17. Projection • We find the product of SW-1 and SB and then compute the Eigen vectors of this product (SW-1. SB). • Use same technique as eigenfaces approach to reduce the dimensionality of scatter matrix to compute eigenvectors. • Form a matrix U that represents all eigenvectors of SW-1. SB by placing each eigenvector ui as each column in that matrix. • Each face image xj Xi can be projected into this face space by the operation Ωi = UT(xj – )

  18. Testing • Same as Eigenfaces Approach

  19. References • Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3 (1991) 71–86 • Belhumeur, P., P.Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 711–720

More Related