1. Searching and Browsing Video in Face Space Lee Begeja Zhu Liu Video and Multimedia Technologies Research

2. Page 2 Face Oriented Video Browsing Challenge - non linguistic browsing Browse a video using faces Anchorpersons in news broadcasts Main casts in movies Hosts and guests in talk shows

3. Face Finding Face Detection Find a face Face Recognition Find a specific faceface Face Clustering Find a set of similar faces

4. Face Clustering

5. What’s in a Face ? Feature extraction - 50 Features 9 – 3 color moments in Luv space Moments – mean, variance, skew Luv – L -luminance; u,v–chrominance 24 – Gabor textures – 3 scales x 4 directions, mean and std dev 17 – Edge detection histogram in 16 bins across the 2∏ polar coordinate space; with one bin for non-edge pixels

6. Face Clustering Torso region alone: The face dissimilarity is defined as the torso region distance, TD. Torso region and Icon region: The face dissimilarity is defined as weighted summation of torso and icon region distance, α∙TD + (1- α)∙ID, where α is the weighting factor. Torso region and Face region: The face dissimilarity is defined as the minimum of the torso region distance and face distance based on eigenface projection, min(TD, FD). Icon region alone: The face dissimilarity is defined as the icon region distance, ID.

7. Video Browsing InterfaceInterface Page 7

8. Performance Metrics Average Cluster Purity (ACP) – perfect ACP of 1.0 means each cluster only contains faces from one person. Average Face (Class) Purity (AFP) – perfect AFP of 1.0 would have all the faces of one person appearing in one cluster. Analogous to precision(ACP) vs. recall(AFP)

9. Results Face dissimilarityVideo 1 AFP ACP Video 2 AFP ACP Icon region alone0.450.61 Torso region alone0.470.820.570.92 Torso + Face regions (α=0.5) 0.510.830.700.97 Torso + Face regions (eigenface) 0.540.920.721

10. Future Work Working with Sumit Chopra to incorporate dimensionality reduction (DrLIM) Face Search/Clustering across programs Discussions with Patrick Haffner on using SVMs for Face Recognition Do specific face recognition (Obama, Leno) Search for multiple faces within a frame Improve Face Detection Include user generated video in our results

11. Additional Slides

12. Thatcher Effect

13. Gabor textures 3 scales x 4 directions Directions Scales

14. Eigenface Eigenface approach is a PCA (Principal Component Analysis) method, in which a small set of characteristic pictures are used to describe the variation between face images. Recognition is performed by projecting a new image onto the subspace spanned by the eigenfaces and then classifying the face by comparing its position in the face space with the positions of known individuals. Informally, eigenfaces are a set of "standardized face ingredients", derived from analysis of many pictures of faces. Any human face can be considered to be a combination of these standard faces. For example, a face might be composed of the average face plus 20% from eigenface 1, 35% from eigenface 2, and -12% from eigenface 3.

15. Eigenfaces