專題研討 ---心得報告Face Recognition System with Genetic

Algorithm and ANT Colony OptimizationInternational 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

112/04/18 1

Outline (1/2)

• ABSTRACT• INTRODUCTION• HEAD POSE ESTIMATION– Face image Acquisition– Filtering and Clipping

• PROPOSED ANT COLONY OPTIMIZATION GENETIC ALGORITHM

112/04/18 2

Outline (2/2)

• ACOG ALGORITHM• EXPERIMENTAL RESULTS• CONCLUSION

112/04/18 3

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.

112/04/18 4

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.

112/04/18 5

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.

112/04/18 6

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.

112/04/18 7

HEAD POSE ESTIMATION(3/3)

• Filter and Clipping– Filter has been used for fixing these problems.– Clipped to obtain the necessary data.

112/04/18 8

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.

112/04/18 9

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)

112/04/18 10

ACOG ALGORITHM (2/8)

• Initialization:– variable– states– function– input– output– input trajectory– output trajectory

112/04/18 11

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.

112/04/18 12

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.

112/04/18 13

ACOG ALGORITHM (5/8)

112/04/18 14

ySizexSizeB

yxfyxf

nf Wyxtn

max

),(, ),(),(

1)(

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].

112/04/18 15

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

112/04/18 16

ACOG ALGORITHM (8/8)

• Mutation:– To set of mutation rate Pm.– Random number to flip value from 0 to 1 or 1 to 0.

112/04/18 17

EXPERIMENTAL RESULTS (1/2)

• From this Table:

• The results in next page.

112/04/18 18

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.

112/04/18 19

CONCLUSION

• In this paper, this method is more robust suitable for low resolution.

112/04/18 20