Optimization of Feature Selection in Face Recognition System Using Differential Evolution and Genetic Algorithm

Maheshwari, Radhika; Kumar, Manoj; Kumar, Sushil

doi:10.1007/978-981-10-0451-3_34

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…For the recent decades, random forest is widely used in computer vision application, in fact it is a competitive classification algorithm and received growing interests [18,19]. In Salhi et al, [20] were dealing with detection and recognition of face and facial expression using the random forest.…”

Section: Previous Workmentioning

confidence: 99%

Face recognition using fractional coefficients and discrete cosine transform tool

Moussa

Hmila

Douik

2021

IJECE

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Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field.

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Section: Previous Workmentioning

confidence: 99%

Face recognition using fractional coefficients and discrete cosine transform tool

Moussa

Hmila

Douik

2021

IJECE

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“…These are based on physical systems and biological behavior in nature. Examples of such algorithms are genetic algorithm (GA) [9], immune algorithm, differential evolution (DE) algorithm [11,13,24], cuckoo search (CS), particle swarm optimization (PSO) [7,8,10,16,29], ant colony optimization (ACO), harmony search, and many more. These algorithms work on the basis of a mechanism that improves the solution vector at each step, capitulate optimal design parameters, and overcome the computational drawbacks of traditional mathematical optimization methods [28,30].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Face Recognition System Using Cuckoo Search

Malhotra

Kumar

2019

Journal of Intelligent Systems

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The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform (DCT) and principal component analysis (PCA). In this research, a nature-inspired well-known algorithm, namely cuckoo search, has been implemented for face recognition. Further, a hybrid method consisting of DCT and PCA is applied to extract the various features by which recognition can be made with a high rate of accuracy. To validate the proposed methodology, the results are also compared with the existing methodologies, such as PSO, differential evolution, and GA.

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A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection

Elaziz

Ewees

Oliva

et al. 2017

Lecture Notes in Computer Science

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Optimization of Feature Selection in Face Recognition System Using Differential Evolution and Genetic Algorithm

Cited by 6 publications

References 10 publications

Face recognition using fractional coefficients and discrete cosine transform tool

Face recognition using fractional coefficients and discrete cosine transform tool

An Optimized Face Recognition System Using Cuckoo Search

A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection

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