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Anti-Faces for Detection

Anti-Faces for Detection. Daniel Keren Rita Osadchy Haifa University Craig Gotsman Technion. Journal Version: http://www.cs.technion.ac.il/~gotsman/AmendedPubl/Anti-Faces/anti-faces-pami.pdf. Problem Definition.

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Anti-Faces for Detection

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  1. Anti-Faces for Detection Daniel Keren Rita Osadchy Haifa University Craig Gotsman Technion Journal Version: http://www.cs.technion.ac.il/~gotsman/AmendedPubl/Anti-Faces/anti-faces-pami.pdf

  2. Problem Definition Given a set T of training images from an object class , locate all instances of any member of  in test image P. Images from training set Test image

  3. Our Contribution • Simple detection process (inner product). Can be implemented by convolution. • Very fast: For an image of N pixels, usually requires operations, where • Implicit representation. • Uses natural image statistics. • Simple independent detectors.

  4. Previous Work • Eigenfaces and Eigenface Based Approaches. • Neural Networks. • Support Vector Machines. • Fisher Linear Discriminant.

  5. Eigenfaces for RecognitionM. Turk and A. Pentland W B

  6. x DFFS DIFS Eigenface Based Approaches • Probabilistic Visual Learning for Object Representation. B. Moghaddam and A. Pentland • Visual Learning Recognition of 3-D from Appearance. H. Murase and S. Nayar

  7. Neural Networks for Face Detection • Neural Network Based Face Detection. H. Rowly, S. Baluja, and T. Kanade • Rotation Invariant Neural Network Based Face Detection. H. Rowly, S. Baluja, and T. Kanade

  8. Training Support Vector Machines • Training Support Vector Machines: an Application to Face Detection. E. Osuna, R. Freund, and F. Girosi • Training Support Vector Machines for 3-D Object Recognition. M. Pontil and A. Verri • A General Framework for Object Detection. C.P. Papageorgiou, M. Oren, and T. Poggio

  9. margin Support Vectors Training Support Vector Machines “Separating functioal”

  10. Fisher Linear Discriminant • Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. P. N. Belhumeur, P. Hespanha, and D. J. Kriegman

  11. Drawbacks of the Described Methods • Eigenface based methods: • Very high dimension of face-space is needed. • Distance to face-space is a weak discriminator between class images and non-class natural images. • Neural networks, SVM: • Long learning time. • Strong training data dependency. • Many operations on input image are required. • Fisher Linear Discriminant : • Too simple.

  12. Implicit Set Representation • Implicit set representation is more appropriate than an explicit one, for determining whether an element belongs to a set. The value of is a very simple indicator as to whether is close to the circle or not.

  13. In general: characterize a set P by If is the class to be detected, the following should hold: P . should be simple to compute. should be small. If , there should be a low probability that, for every , .

  14. Implicit Set Representation The natural extension of this idea to detection is: Find functionals which attain asmallvalue on the object class , and use them for detection. The first guess: inner product with vectors orthogonal to ‘s elements. So, iff ,… . However… this fails miserably:

  15. Orthogonal detectors for pocket calculator Many false alarms (and failure to detect true instance) when using these detectors

  16. Implicit Set Representation Conclusion: It’s not enough for the detectors to attain small values on the object class, they also have to attain larger values on “random” images. Our model for random smooth.

  17. Implicit Approach for Detection To Summarize: • The functionals used for detection are linear: where dis a detector for a class  , I an input image, and n the image size. • The functional F(I)must be large for random natural (smooth) images, and small for the images of . Otherwise, there are many false alarms.

  18. Class Detection Using Smooth Detectors • Boltzman distribution for smooth images: It follows that are the DCT coefficients of d. where to be large, since for d is smooth. should be concentrated in small

  19. Class Detection Using Smooth Detectors • The average response of a smooth detector on a smooth image is large. • This relation was checked on 6,500 different detectors, each one on 14,440 natural images.

  20. Relationship between theoretical and empirical expectation of squared inner product with detector d

  21. Class Detection Using Smooth Detectors • Trade-off between • Smoothness of the detector. • Orthogonality to the training set. • Detection:

  22. Schematic Description of the Detection Schematic Description of the Detection “Direction of smoothness” Templates Natural images Eigenface method positive set Anti-face method positive set

  23. False Alarms in Detection • P - f.a. probability. P << 1. m independent detectors give • The detectors are independent if are independent random variables. This holds iff

  24. Finding the Detectors • Choose an appropriate value M for It should be substantially smaller than the absolute value of the inner product of two “random images”. 2 Minimize The optimization is performed in DCT domain, and the inverse DCT transform of the optimum is the desired detector.

  25. Finding the Detectors • Using a binary search on , set it so that • Incorporate the condition for independent detectors into the optimization scheme to find the other detectors.

  26. Three of the “Esti” images The first four “anti-Esti” detectors Detection result: all ten “Esti” instances were located, without false alarms

  27. Eigenface method with the subspace of dimension 100

  28. Detection Results Number of Eigenvalues for 90% Energy

  29. Detection Results The results refer to “rotate + scale” case.

  30. Fisher Linear Discriminant Results: “Esti” class Three random image sets

  31. (B) (A) (C) (A) and (B) Anti-Faces with 8 detectors. (C) Eigenface method with the subspace of dimension 8. Eigenface method requires the subspace of dimension 30 for correct detection.

  32. Detection of 3D objects from the COIL database

  33. Detection of COILobjects on arbitrary background

  34. Detection Under Varying Illumination: Model object and shadows. Detect objects and shadows in the logarithm of the image. Remove “shadow only” instances, using “shadow only” detectors.

  35. Osadchy, Keren: “Detection Under Varying Illumination and Pose”, ICCV 2001.

  36. Event Detection crocodile psychology psychological anthology “Anti-psychology”

  37. Future Research • Develop a general face detector. • Develop a detector with non-convex positive set. • Speech recognition.

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