The document introduces Facool, which is a face and name association algorithm that uses an iterative process to calculate similarity scores between faces in a database to determine the best name match. It provides an example application of the Facool algorithm to associate names with faces, showing the calculations over multiple iterations until threshold values are met. Additionally, it describes an index-based approach to Facool that involves setting up arrays to store face images and vote values for efficient retrieval of face matches from the database.

1. What is F a c o o l ? A Brief Introduction Facool

3. Face & Name Association 1. Initialize , t = 0; 2. Enumerate faces in database, calculate 3. , if , terminate procedure and is the final membership function. Let t = t + 1, and go to step 2.

4. F & N Association Contrived Example Sim = 0.50 Rec =-0.14 Sim = 0.90 Rec = 0.60 Sim = 0.60 Rec =-0.09 Bill Clinton = 4/9 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 3/5 Dick = 2/5 Dick Morris = 2/5 Bill Clinton = 2/5 John Kerry = 1/5 Bill Clinton = 4/9 + (3/5 * 0.6 - 2/5 * 0.14)/2 = 0.624 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 3/5 + (4/9 * 0.6 - 2/5 * 0.09)/2 = 0.715 Dick = 2/5 - 2/5 * 0.09 = 0.364 Dick Morris = 2/5 - 2/5 * 0.09 = 0.364 Bill Clinton = 2/5 - (4/9 * 0.14 + 3/5 * 0.09)/2 = 0.342 John Kerry = 1/5 Threshold = 0.75

5. F & N Association Contrived Example Sim = 0.50 Rec =-0.14 Sim = 0.90 Rec = 0.60 Sim = 0.60 Rec =-0.09 Bill Clinton = 0.624 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 0.715 Dick = 0.364 Dick Morris = 0.364 Bill Clinton = 0.342 John Kerry = 1/5 Bill Clinton = 4/9 + (0.715 * 0.6 - 0.342 * 0.14)/2 = 0.6 35 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 3/5 + (0.624 * 0.6 - 0.342 * 0.09)/2 = 0.7 72 Dick = 2/5 - 0.364 * 0.09 = 0.36 7 Dick Morris = 2/5 - 0.364 * 0.09 = 0.36 7 Bill Clinton = 2/5 - (0.624 * 0.14 + 0.715 * 0.09)/2 = 0.3 24 John Kerry = 1/5 Threshold = 0.75

6. F & N Association Contrived Example Sim = 0.50 Rec =-0.14 Sim = 0.90 Rec = 0.60 Sim = 0.60 Rec =-0.09 Bill Clinton = 0.6 35 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 0.7 72 Dick = 0.36 7 Dick Morris = 0.36 7 Bill Clinton = 0.3 24 John Kerry = 1/5 Bill Clinton = 4/9 + (0.772 * 0.6 - 0.324 * 0.14)/2 = 0.6 5 3 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 3/5 + (0.635 * 0.6 - 0.324 * 0.09)/2 = 0.7 7 6 Dick = 2/5 - 0.367 * 0.09 = 0.36 7 Dick Morris = 2/5 - 0.367 * 0.09 = 0.36 7 Bill Clinton = 2/5 - (0.635 * 0.14 + 0.772 * 0.09)/2 = 0.3 2 1 John Kerry = 1/5 Threshold = 0.75

7. F & N Association Contrived Example Sim = 0.50 Rec =-0.14 Sim = 0.90 Rec = 0.60 Sim = 0.60 Rec =-0.09 Bill Clinton = 0.6 5 3 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 0.7 7 6 Dick = 0.36 7 Dick Morris = 0.36 7 Bill Clinton = 0.3 2 1 John Kerry = 1/5 Bill Clinton = 4/9 + (0.776 * 0.6 - 0.321 * 0.14)/2 = 0.6 5 4 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 3/5 + (0.653 * 0.6 - 0.321 * 0.09)/2 = 0.7 81 Dick = 2/5 - 0.367 * 0.09 = 0.36 7 Dick Morris = 2/5 - 0.367 * 0.09 = 0.36 7 Bill Clinton = 2/5 - (0.653 * 0.14 + 0.776 * 0.09)/2 = 0.3 19 John Kerry = 1/5 Threshold = 0.75

8. F & N Association Contrived Example Sim = 0.50 Rec =-0.14 Sim = 0.90 Rec = 0.60 Sim = 0.60 Rec =-0.09 Bill Clinton = 0.6 5 4 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 0.7 81 Dick = 0.36 7 Dick Morris = 0.36 7 Bill Clinton = 0.3 19 John Kerry = 1/5 Bill Clinton = 4/9 + (0.781 * 0.6 - 0.319 * 0.14)/2 = 0.6 5 7 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 3/5 + (0.654 * 0.6 - 0.319 * 0.09)/2 = 0.7 8 2 Dick = 2/5 - 0.367 * 0.09 = 0.36 7 Dick Morris = 2/5 - 0.367 * 0.09 = 0.36 7 Bill Clinton = 2/5 - (0.654 * 0.14 + 0.781 * 0.09)/2 = 0.3 19 John Kerry = 1/5 Threshold = 0.75

9. F & N Association Contrived Example Sim = 0.50 Rec =-0.14 Sim = 0.90 Rec = 0.60 Sim = 0.60 Rec =-0.09 Bill Clinton = 0.657 Peter Mcdermott = 1/9 Paul Farmer = 4/9 Bill Clinton = 0.783 Dick = 0.367 Dick Morris = 0.367 Bill Clinton = 0.319 John Kerry = 1/5 Threshold = 0.75 ……… ... Bill Clinton Bill Clinton Dick Morris

10. Index & Retrieval Approach 1. Setup set table Idx [1~ D , 0~ L ]; 2. Idx [ i , f k ( i )] ∪{ k }-> Idx [ i , f k ( i )]. Note: f k is the k face image. Indexing & Storing: 1. Setup array Vot [1~ n ]; 2. Vot [ Idx [ i , g ( i )]]+ w ( i )-> Vot [ Idx [ i , g ( i )]]. Note: g is the input face image, w is the weight at i . Retrieving:

11. Index Approach Contrived Example Gray Level Dimension 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Image 1: Image 2: 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

12. Retrieval Approach Contrived Example 1 Gray Level Dimension 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? Retrieving Image: Vote 1: Vote 2: ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

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