Feature Selection Using Genetic Algorithm for Face Recognition Based on PCA, Wavelet and SVM

Satone, Manisha; Kharate, G. K.

doi:10.15676/ijeei.2014.6.1.3

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…For example, research has been done in the field of methods such as fractal coding to increase speed, which uses the feature of similarity in different areas of the image [57]. One of the successful methods used in face recognition is principal component analysis [58]. In the proposed method, feature extraction for recognition is done using the PCA algorithm.…”

Section: -6-training Test and Accuracy Calculationmentioning

confidence: 99%

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

khodadadi

Ardeshir

Grailu

2022

Preprint

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One of the important research areas in imaging is the formation of images, which plays an important role in many different applications, including surveillance, control, and security affairs. On the other hand, high spatial resolution is one of the most important factors for increasing image quality, but it increases the amount of storage memory. Based on this, it is expected that the processing methods will be concentrated in this area. In face recognition systems, one of the existing challenges is maintaining the image recognition rate. These images in the recognition system may affect the efficiency of the system, and as a result, it may be inferred that the percentage of recognition is found. Proposing a method that at least does not reduce detection rates would be very desirable. This article investigates how to compress facial images with high spatial resolution using innovative algorithms to reduce or even increase their accuracy as much as possible. In this article, meta-heuristic algorithms (genetics and gray wolf) are used in a way that they are responsible for identifying the important and similar areas of matching macroblocks in the whole image segmentation. So how arranging the length of the bit string of each block is appropriate to achieve the target estimation to achieve the highest recognition accuracy value, PSNR value, and SSIM value of the image set? In the simulation and evaluation section, the facial images of the CIE and FEI databases have been examined as a selective study, and the recognition efficiency of the images for the conditions without/with compression and the common SPIHT and JPEG coding methods and compared with the proposed method. The simulation results show the significant impact of the proposed methods using meta-heuristic algorithms in increasing the quality of PSNR and SSIM in contrast to the recognition efficiency. According to the proposed method, the larger the value of dividing the blocks, the better the average PSNR and SSIM, assuming the highest recognition accuracy can be reached in both meta-heuristic algorithms. In general, depending on the type of application of the problem, there is a compromise to achieve the highest average PSNR or SSIM, using a genetic algorithm or gray wolf. The gray wolf algorithm, however, reaches its optimal answer much faster than the genetic algorithm.

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Section: -6-training Test and Accuracy Calculationmentioning

confidence: 99%

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

khodadadi

Ardeshir

Grailu

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Researchers have studied methods such as fractal coding to increase speed, which utilizes similarity in different areas of the image [54]. Principal component analysis is a method that has proven successful in the recognition of faces [55]. PCA is used in the proposed method to extract features for recognition.…”

Section: Accuracy Measurementmentioning

confidence: 99%

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

khodadadi

Ardeshir

Grailu

2023

IJE

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In image processing, compression plays an important role in monitoring, controlling, and securing the process. The spatial resolution is one of the most effective factors in improving the quality of an image; but, it increases the amount of storage memory required. Based on meta-heuristic algorithms, this article presents a compression model for face images with block division and variable bit allocation. Wavelet transform is used to reduce the dimensions of high spatial resolution face images. In order to identify important and similar areas of identical macroblocks, genetic algorithms and gray wolves are used. A bit rate allocation is calculated for each block to achieve the best recognition accuracy, average PSNR, and SSIM. The CIE and FEI databases have been used as case studies. The proposed method has been tested and compared with the accuracy of image recognition under uncompressed conditions and using the common SPIHT and JPEG coding methods. Recognition accuracy increased from 0.18% for 16×16 blocks to 1.97% for 32×32 blocks. Additionally, the gray wolf algorithm is much faster than the genetic algorithm in reaching the optimal answer. Depending on the application type of the problem, the genetic algorithm or the gray wolf may be preferred to achieve the maximum average PSNR or SSIM. At the bit rate of 0.9, the maximum average PSNR for the gray wolf algorithm is 34.92 and the maximum average SSIM for the genetic algorithm is 0.936. Simulation results indicate that the mentioned algorithms increase PSNR and SSIM by stabilizing or increasing recognition accuracy.

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“…It is based on the development of a separation area between the various learning set classes (also called test set). The boundaries of this area are known as hyperplanes [12,30,31]. A good classification via this method is based on the increase of the margin located between the different hyperplanes [31,32].…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

“…The boundaries of this area are known as hyperplanes [12,30,31]. A good classification via this method is based on the increase of the margin located between the different hyperplanes [31,32].…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

ECG Signal Classification Using DWT, MFCC and SVM Classifier

Toulni¹,

Nsiri²,

Drissi³

2023

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The diagnosis techniques of diseases which are based on biomedical signals processing are constantly evolving, cardiovascular diseases are no exception to the other biomedical signals. Thanks to the development of signal processing techniques, it has been possible to extract several kinds of information from the ECG signals who told us about the heart's health. The goal of this study is to attempt to create a model based on two methods of signal processing: wavelet analysis and the determination of Mel frequency cepstral coefficients. With the help of this model, it is possible to extract statistical features and MFCC coefficients from approximation coefficients obtained when the discrete wavelet transform (DWT) is applied to analyze an ECG signal. As a result, the various features derived for each approximation coefficient will be classified using a support vector machine classifier (SVM classifier). The classifier's performance has been measured after the use a k fold cross validation technique to avoid the overfitting and the underfitting problems and making the results more reliable and credible.

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Feature Selection Using Genetic Algorithm for Face Recognition Based on PCA, Wavelet and SVM

Cited by 11 publications

References 14 publications

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

ECG Signal Classification Using DWT, MFCC and SVM Classifier

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