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Inter-modality Face Sketch Recognition

Inter-modality Face Sketch Recognition. Hamed Kiani. Outline. Overview Previous Works Proposed Approach Results Summary. Overview. Face Recognition. Input Face. Known Face Images. Face Recognition System. Identity. Overview. Face sketch recognition. Verbal description. Viewing.

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Inter-modality Face Sketch Recognition

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  1. Inter-modality Face Sketch Recognition HamedKiani

  2. Outline • Overview • Previous Works • Proposed Approach • Results • Summary Inter-modality Face Sketch Recognition ICME'12

  3. Overview • Face Recognition Input Face Known Face Images Face Recognition System Identity Inter-modality Face Sketch Recognition ICME'12

  4. Overview • Face sketch recognition Verbal description Viewing Drawing Police artist Sketch Eyewitness Known Face Photos (Mug shot) Photo-Sketch Matching Suspect’s identity Inter-modality Face Sketch Recognition ICME'12

  5. Overview • Modality Gap: the difference of visual cues between face sketch and photo. Inter-modality Face Sketch Recognition ICME'12

  6. Overview • Visual cues of face come from: • Fine texture (appearance): low contrast details, flaws, moles, wrinkles , etc. • Coarse texture (shape): high contrast boundaries of facial components eyes, mouth, etc Inter-modality Face Sketch Recognition ICME'12

  7. Overview • Face textures and modality gap: • Fine textures of a face photo captured by camera (true pixels) • Fine texture of a sketch is rendered by artist, depending on drawing style and tools • Fine textures of face photo and sketch are not equivalent: high amount of modality gap • Coarse texture (facial component and boundaries) exists in both sketch and photo • modality gap is not affected significantly by coarse texture Inter-modality Face Sketch Recognition ICME'12

  8. Proposed Approach • Histogram of Averaged Oriented Gradients (HAOG): a modified version of Histogram of Oriented Gradients (HOG) • HOG for sketch recognition: Modeling local appearance and shape Based on fineand coarse textures. “Fine texture leads to a high amount of modality gap” Inter-modality Face Sketch Recognition ICME'12

  9. Proposed Approach • Idea of HAOG: Emphasizing coarse texture much more than fine texture in feature extraction. • How? By averagedgradient vector (dominant gradient) instead of pixel’s gradient vector (orientation and magnitude). Inter-modality Face Sketch Recognition ICME'12

  10. Proposed Approach • But: Local gradients cannot directly be averaged, opposite gradient vectors cancel each other • Solution: Doubling the angles of the gradient vectors before averaging: equal to squaring the length of gradient vectors [Bazen and Grez, 2002]. Inter-modality Face Sketch Recognition ICME'12

  11. Proposed Approach • Thus, we define squared gradient vectors Inter-modality Face Sketch Recognition ICME'12

  12. Proposed Approach • HAOG x-gradient y-gradient Inter-modality Face Sketch Recognition ICME'12

  13. Proposed Approach • HAOG HAOG Inter-modality Face Sketch Recognition ICME'12

  14. Proposed Approach • Given a query sketch and a gallery of face photos , face sketch recognition is done by: : HAOG descriptor , :chi-square Inter-modality Face Sketch Recognition ICME'12

  15. Proposed Approach Figure 1. (a1) Face photo, (a2) Face sketch, (b1,b2) Gradient magnitudes of (a1,a2), Squared gradient magnitudes of (a1,a2). Inter-modality Face Sketch Recognition ICME'12

  16. Proposed Approach Figure 2. Face sketch (top), photo (bottom), (b,c,d) local patches (first row), HAOG descriptors (second row) and HOG descriptors (third row). Inter-modality Face Sketch Recognition ICME'12

  17. Results • Results on CUHK dataset with 606 pairs of face photo/sketch Inter-modality Face Sketch Recognition ICME'12

  18. Summary • Face sketch recognition vs. face recognition • Modality gap • HOG vs. HAOG • Future work Inter-modality Face Sketch Recognition ICME'12

  19. Inter-modality Face Sketch Recognition ICME'12

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