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Visual Search Engine-Faces

Visual Search Engine-Faces. Joseph Sabet ECE 172A Final project. Motivation. Search engines- currently use text search for image searches. Sometimes this can make it hard to find a desired type of image online

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Visual Search Engine-Faces

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  1. Visual Search Engine-Faces Joseph Sabet ECE 172A Final project

  2. Motivation • Search engines- currently use text search for image searches. Sometimes this can make it hard to find a desired type of image online • Electronic photo albums- Using these techniques, personal photos could be searched and classified into genres of pictures automatically

  3. Research • [1] Essannouni, L., Elhaj, E.I., Aboutajdine, D., “Automatic Face tracking and identity verification,” in 14th IEEE International Conference on Electronics, Circuits and Systems, USA, 2007. • [2] Hyong Woo Lee,   SeKee Ki,l   Younghwan Han,   SeungHong Hong, “Automatic face and facial features detection,” in IEEE International Symposium on Industrial Electronics, Inchon, South Korea, 2001. • [3] Pakazad, S.K. Faez, K. Hajati, F., “Face Detection Based on Central Geometrical Moments of Face Components” in IEEE International Conference on Systems, Man and Cybernetics, Tehran, Iran, 2006. • [4] Campadelli, P., Lanzarotti, R. Lipori, G., “Face Detection in Color Images of Generic Scenes,” in IEEE International Conference on Computational Intelligence for Homeland and Security and Personal Safety, Milano, Italy, 2004.

  4. Approach • Use HSV and YCbCr colorspace • For Skin Thresholding use Cb and Cr values • For feature extraction use H, S, Cr • Clean up Skin regions • Extract Lip/eye regions that lie in Skin region • Verify that features exist

  5. Approach (cont’) • Template matching with an elliptical template • Adjust the size of the template (1/1.2) until the template is 20x20 pixels (smallest face that can be detected) • During matching process, verify that the upper region contains eyes and the lower region contains lips. • As soon as a suitable candidate is detected, exit program

  6. Results • Used Caltech 101 database • Face only search (160 images)- 3 misses (98%) • Random search (130 images) – 2 miss, 1 false detection (97% accurate) • Mostly works fine, except when the facial features are indistinguishable • False Detection occurred with photos that had too many shapes and textures

  7. Future Improvements • Run Algorithm with C/C++ to speed up program for larger images • Better thresholding to eliminate false detection • Adjust for lighting to detect faces in dark images • More advanced feature identification

  8. Milestones/ hurdles • Thresholding the Skin, Lip, Eye regions • Trying to make the Program run faster • Filtering image for arbitrary sized faces

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