1. 專題研討 --- 心得報告 Face Recognition System with Genetic Algorithm and ANT Colony Optimization International Journal of Innovation, Management and Technology, Vol. 1, No. 5, December 2010 S.Venkatesan and Dr.S.Srinivasa Rao Madane 報告者 : 黃柏翎 指導老師：鄭朝榮、王文彥 課程指導老師：蘇德仁 2015/5/161

2. Outline (1/2) ABSTRACT INTRODUCTION HEAD POSE ESTIMATION – Face image Acquisition – Filtering and Clipping PROPOSED ANT COLONY OPTIMIZATION GENETIC ALGORITHM 2015/5/162

3. Outline (2/2) ACOG ALGORITHM EXPERIMENTAL RESULTS CONCLUSION 2015/5/163

4. ABSTRACT A novel face recognition system to detection faces in images. This system is caped with three steps: – Initially preprocessing methods are applied on the input images. – Consequently face features are extracted from the processed image by ANT Colony Optimization. – Recognition by Genetic Algorithm. 2015/5/164

5. INTRODUCTION Face recognition is the process of automatically detection whether two faces are the same person. Face recognizers, like our detectors, have been trained using novel statistical learning methods, to deal with these diverse factors and provide accurate results on real-world data. 2015/5/165

6. HEAD POSE ESTIMATION(1/3) Their face detection technology not only locates faces, but it also estimates the 3D head pose. Detect one set of landmarks in frontal and semi-profile faces. Detect a second set of landmarks in full-profile faces. 2015/5/166

7. HEAD POSE ESTIMATION(2/3) Face Image Acquisition: – To collect the face images, a scanner has been used. – Saved into various formats such as Bitmap, JPEG, GIF and TIFF. 2015/5/167

8. HEAD POSE ESTIMATION(3/3) Filter and Clipping – Filter has been used for fixing these problems. – Clipped to obtain the necessary data. 2015/5/168

9. PROPOSED ACOG The ACO system contains two rules: – Local pheromone update rule, which applied whilst constructing solutions. – Global pheromone update rule, which applied after all ants constrict a solution. 2015/5/169

10. ACOG ALGORITHM (1/8) ACOG is differing from previous algorithm. It consists of two main sections: – Initialization – Main loop (Genetic Programming is used in the second sections) 2015/5/1610

11. ACOG ALGORITHM (2/8) Initialization: – variable – states – function – input – output – input trajectory – output trajectory 2015/5/1611

12. ACOG ALGORITHM (3/8) While termination conditions not meet do Construct Ant Solution: – Apply Local Search – Best Tour check: If there is an improvement, update it. – Update Trails: Evaporate a fixed proportion of the pheromone on each read. For each ant perform the “ant-cycle” pheromone update. 2015/5/1612

13. ACOG ALGORITHM (4/8) Initial Population: – Generate randomly a new population of chromosomes of size N: x1, x2….xn. – Assign the crossover probability Pc and the mutation probability Pm. 2015/5/1613

14. ACOG ALGORITHM (5/8) 2015/5/1614

15. ACOG ALGORITHM (6/8) Selection: – Select a pair of chromosomes for mating use the roulette wheel selection procedure. – To select highly fit of chromosome for mating a random number is generated in the interval[0, 100]. 2015/5/1615

16. ACOG ALGORITHM (7/8) Crossover: – To chooses a crossover point where two parent chromosomes break and then exchanges the chromosomes parts after that point. Single point Two point uniform 2015/5/1616

17. ACOG ALGORITHM (8/8) Mutation: – To set of mutation rate Pm. – Random number to flip value from 0 to 1 or 1 to 0. 2015/5/1617

18. EXPERIMENTAL RESULTS (1/2) From this Table: The results in next page. 2015/5/1618

19. EXPERIMENTAL RESULTS (2/2) Therefore the efficiency of the Face Recognition System by using Genetic and Ant Colony Optimization Algorithm is Best than other methods. 2015/5/1619

20. CONCLUSION In this paper, this method is more robust suitable for low resolution. 2015/5/1620