專題研討 --- 心得報告 face recognition system with genetic algorithm and ant colony...
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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
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Outline (1/2)
• ABSTRACT• INTRODUCTION• HEAD POSE ESTIMATION– Face image Acquisition– Filtering and Clipping
• PROPOSED ANT COLONY OPTIMIZATION GENETIC ALGORITHM
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Outline (2/2)
• ACOG ALGORITHM• EXPERIMENTAL RESULTS• CONCLUSION
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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.
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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.
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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.
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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.
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HEAD POSE ESTIMATION(3/3)
• Filter and Clipping– Filter has been used for fixing these problems.– Clipped to obtain the necessary data.
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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.
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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)
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ACOG ALGORITHM (2/8)
• Initialization:– variable– states– function– input– output– input trajectory– output trajectory
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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.
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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.
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ACOG ALGORITHM (5/8)
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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].
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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
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ACOG ALGORITHM (8/8)
• Mutation:– To set of mutation rate Pm.– Random number to flip value from 0 to 1 or 1 to 0.
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EXPERIMENTAL RESULTS (1/2)
• From this Table:
• The results in next page.
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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.
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CONCLUSION
• In this paper, this method is more robust suitable for low resolution.
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