marin
Uploaded by
32 SLIDES
457 VUES
320LIKES

Exploring Eigenfaces: A Comprehensive Database and Methodology for Facial Recognition

DESCRIPTION

This detailed study examines the Eigenfaces method for facial recognition, showcasing a robust database comprising 7,562 images of 3,000 individuals from the MIT Media Lab. The work emphasizes various techniques, including photometric stereo and spherical harmonics for reconstructing 3D models from images. It discusses the strengths of PCA and ROC curve analysis in recognition performance. By exploring empirical studies and mathematical foundations, this research aims to enhance understanding of facial recognition technologies and their applications in diverse fields.

1 / 32

Télécharger la présentation

Exploring Eigenfaces: A Comprehensive Database and Methodology for Facial Recognition

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

Playing audio...

  1. Eigenfaces Photobook/Eigenfaces (MIT Media Lab)

  2. Database Photobook/Eigenfaces (MIT Media Lab) 7562 pictures of 3000 people

  3. Query Example Photobook/Eigenfaces (MIT Media Lab)

  4. Eigenfeatures Photobook/Eigenfaces (MIT Media Lab)

  5. Eigenfeatures Photobook/Eigenfaces (MIT Media Lab)

  6. Eigenfeatures Photobook/Eigenfaces (MIT Media Lab)

  7. Eigenfeatures Photobook/Eigenfaces (MIT Media Lab) Receiver Operating Characteristic (ROC) Curve

  8. Recognition with PCA Amano, Hiura, Yamaguti, and Inokuchi; Atick and Redlich;Bakry, Abo-Elsoud, and Kamel;Belhumeur, Hespanha, and Kriegman;Bhatnagar, Shaw, and Williams; Black and Jepson; Brennan and Principe;Campbell and Flynn; Casasent, Sipe and Talukder;Chan, Nasrabadi and Torrieri;Chung, Kee and Kim; Cootes, Taylor, Cooper and Graham; Covell; Cui and Weng;Daily and Cottrell;Demir, Akarun, and Alpaydin;Duta, Jain and Dubuisson-Jolly; Hallinan; Han and Tewfik; Jebara and Pentland;Kagesawa, Ueno, Kasushi, and Kashiwagi;King and Xu;Kalocsai, Zhao, and Elagin; Lee, Jung, Kwon and Hong; Liu and Wechsler;Menser and Muller;Moghaddam;Moon and Philips; Murase and Nayar; Nishino, Sato, and Ikeuchi;Novak, and Owirka;Nishino, Sato, and Ikeuchi;Ohta, Kohtaro and Ikeuchi; Ong and Gong; Penev and Atick; Penev and Sirivitch;Lorente and Torres;Pentland, Moghaddam, and Starner;Ramanathan, Sum, and Soon; Reiter and Matas; Romdhani, Gong and Psarrou;Shan, Gao, Chen, and Ma;Shen, Fu, Xu, Hsu, Chang, and Meng;Sirivitch and Kirby; Song, Chang, and Shaowei; Torres, Reutter, and Lorente;Turk and Pentland;Watta, Gandhi, and Lakshmanan;Weng and Chen; Yuela, Dai, and Feng; Yuille, Snow, Epstein, and Belhumeur;Zhao, Chellappa, and Krishnaswamy;Zhao and Yang.

  9. Lambertian Reflectance • Matt surface • Light source is distant • Light reflected equally to all directions q or

  10. Photometric Stereo: Factorization • M is f x p (#images x #pixels) • L is f x 3 – light sources • S is 3 x p – surface normals (scaled by albedo) • Rank(M)=3 (if no noise present) • SVD: • Ambiguity Eliminate by forcing integrability

  11. Illumination Cone =0.5* +0.2* +0.3*

  12. Empirical Study

  13. Intuition lighting reflectance

  14. Spherical Harmonics • Orthonormal basis for functions on the sphere • n’th order harmonics have 2n+1 components • Rotation = phase shift (same n, different m) • In space coordinates: polynomials of degree n • Funk-Hecke convolution theorem

  15. Spherical Harmonics 1 Z X Y XY XZ YZ

  16. Harmonic Transform of Kernel n

  17. Cumulative Energy (percents) N

  18. Second Order Approximation

  19. Other Low-D Approximations (Ramamoorthi)

  20. r Harmonic Images

  21. Reconstruction

  22. Reconstruction

  23. Motion + Illumination

  24. Reconstruction Reconstruction Laser scan

  25. Advantage of Our Method Residue Std intensity Disparity error Disparity error Assuming brightness constancy Accounting for illumination variation

More Related