Face Recognition Based on Grey Wolf Optimization for Feature Selection

Saabia, Abd AL-BastRashed; El‐Hafeez, Tarek Abd; Zaki, Alaa M.

doi:10.1007/978-3-319-99010-1_25

Cited by 27 publications

(12 citation statements)

References 16 publications

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“…GWO-based Feature selection: The GWO has been implemented in several domains as a feature selection method, such as face recognition, voice recognition, disease diagnosis, and intrusion detection systems. For example, in [35], Saabia et al proposed a system for face recognition; GWO is used for feature selection, and k-NN is used for classification. The system performance was good in terms of both classification accuracy and time complexity.…”

Section: Related Workmentioning

confidence: 99%

Pyramided and optimized blurred shape model for plant leaf classification

Almodfer

Mudhsh

Jing

2023

IET Image Processing

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Plant leaf classification is a crucial task in the field of computer vision and pattern recognition, with various applications such as plant species identification and disease diagnosis. In this paper, the authors introduce the Pyramid Blurred Shape Model (PBSM) as a new descriptor for plant leaf classification. The PBSM extracts both shape and texture features from plant leaf images, which are combined to define a probability density function for leaf shape. Our experimental results show that the proposed PBSM achieves high classification accuracy, F1‐score, and precision‐recall results, demonstrating its effectiveness for leaf classification. However, extracting all available features from leaf images can lead to redundant and inessential features, which can degrade the classification performance and computational efficiency. To address this issue, the authors implement grey wolf optimization (GWO)‐based feature selection to identify the most informative features for classification. The final set of features is then classified using a list of selected classifiers, further enhancing the performance of the authors’ approach. The authors evaluate their proposed method on three publicly available datasets, namely the Middle European Woods (MEW), Swedish, and Flavia leaf datasets, and achieve high classification accuracies of 96.34%, 96.89%, and 92.41% for the Flavia, Swedish, and MEW leaf datasets, respectively. The authors’ approach outperforms state‐of‐the‐art descriptors in terms of classification accuracy and robustness, demonstrating its potential for real‐world applications. Overall, the authors’ proposed PBSM descriptor with feature selection provides a reliable and efficient solution for plant leaf classification. It can contribute to the development of automated plant species identification systems and disease diagnosis, thereby facilitating the conservation and protection of plant species.

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Section: Related Workmentioning

confidence: 99%

Pyramided and optimized blurred shape model for plant leaf classification

Almodfer

Mudhsh

Jing

2023

IET Image Processing

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“…In 2019, a face recognition method based on grey wolf optimization for feature selection [ 25 ] was proposed. In this paper, the authors used the grey wolf optimizer to prune out the redundant features in the image dataset and in doing so reduce the runtime of the process while increasing the classification accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection

Kitonyi

Segera

2021

BioMed Research International

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Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant features are removed, reducing the complexity of the data and reducing training time. The objective of this paper was to design an optimizer that combines the well-known metaheuristic population-based optimizer, the grey wolf algorithm, and the gradient descent algorithm and test it for applications in feature selection problems. The proposed algorithm was first compared against the original grey wolf algorithm in 23 continuous test functions. The proposed optimizer was altered for feature selection, and 3 binary implementations were developed with final implementation compared against the two implementations of the binary grey wolf optimizer and binary grey wolf particle swarm optimizer on 6 medical datasets from the UCI machine learning repository, on metrics such as accuracy, size of feature subsets, F -measure, accuracy, precision, and sensitivity. The proposed optimizer outperformed the three other optimizers in 3 of the 6 datasets in average metrics. The proposed optimizer showed promise in its capability to balance the two objectives in feature selection and could be further enhanced.

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“…The following are examples that represent groups of such research. One example is when the preprocessing is intended for the appropriate technologies applied, so the conversion of color photography to black and white (shades of gray) is performed due to the use of Gabor filters to extract features that represent the face [4]. Many comparative studies also measure the impact of different illumination preprocessing techniques on face recognition performance, concluding that such techniques give almost perfect results in controlled lighting variations and that better visual preprocessing results do not guarantee better recognition accuracy [11].…”

Section: Influence Of Image Enhancement Techniques On Effectiveness Of Unconstrained Face Detection and Identificationmentioning

confidence: 99%

“…As one of the main biometric traits, the application of face recognition is of increasing importance in the areas of video surveillance, access control, human-computer interaction, border control surveillance, and crime investigation [1]- [3]. The application in a criminal investigation to identify the perpetrators of crimes, which is of particular interest for this paper, stands out primarily because of the emphasis on security in modern society [4]. There are two decisive reasons for the importance of the application of face recognition in criminal investigations.…”

Section: Introductionmentioning

confidence: 99%

Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification

Vuković

Čisar

Kuk

et al. 2021

ELEKTRON ELEKTROTECH

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In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.

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Face Recognition Based on Grey Wolf Optimization for Feature Selection

Cited by 27 publications

References 16 publications

Pyramided and optimized blurred shape model for plant leaf classification

Pyramided and optimized blurred shape model for plant leaf classification

Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection

Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification

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