Optimization of K-nearest neighbor using particle swarm optimization for face recognition

Face biometric has received more attention to recognize a person in a right way. However, the face recognition is considered to be hard due to size, ethnicity, illumination, pose, various expression, and age. In this work, a novel approach is proposed to recognize the human face based on K-nearest n...

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Vydáno v:Neural computing & applications Ročník 31; číslo 11; s. 7935 - 7944
Hlavní autoři: Sasirekha, K., Thangavel, K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.11.2019
Springer Nature B.V
Témata:
Algorithms
Ant colony optimization
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Face recognition
Facial recognition technology
Feature extraction
Genetic algorithms
Heuristic methods
Image Processing and Computer Vision
K-nearest neighbors algorithm
Multilayer perceptrons
Optimization
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Support vector machines
Webcams
Face biometric
GA
KNN
LBP
ACO
PSO
ISSN:0941-0643, 1433-3058
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Shrnutí:Face biometric has received more attention to recognize a person in a right way. However, the face recognition is considered to be hard due to size, ethnicity, illumination, pose, various expression, and age. In this work, a novel approach is proposed to recognize the human face based on K-nearest neighbor (KNN) with particle swarm optimization (PSO). Initially, the features are extracted using local binary pattern. The metaheuristic optimization algorithms such as genetic algorithm, PSO, and ant colony optimization are investigated for feature selection. The KNN classifier is optimized using the population-based metaheuristic algorithm PSO. Finally, the face recognition is performed using the proposed PSO–KNN algorithm. In this research, experiments have been conducted on real-time face images collected from 155 subjects each with ten orientations using Logitech Webcam and also on ORL face dataset. The experimental result of the proposed PSO–KNN is compared with other benchmark recognition techniques such as decision table, support vector machine, multilayer perceptron and conventional KNN, to conclude the efficacy of the proposed approach.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3624-9