Bald eagle search optimization with deep transfer learning enabled age-invariant face recognition model

Alsubai, Shtwai; Hamdi, Monia; Abdel‐Khalek, S.; Alqahtani, Abdullah; Binbusayyis, Adel; Mansour, Romany F.

doi:10.1016/j.imavis.2022.104545

Cited by 21 publications

(9 citation statements)

References 16 publications

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“…With the popularity of graphics processing units, the deep learning approaches ( Alsubai et al., 2022 ; Farghaly et al., 2022 ) have developed the most advanced model in the computer vision field, and numerous elegant solutions ( Xue et al., 2019 ; Xue et al., 2021 ) have been proposed for visual tasks in the last few years. In the field of image enhancement, most researchers work on task-specific image restoration.…”

Section: Related Workmentioning

confidence: 99%

All-in-one aerial image enhancement network for forest scenes

et al. 2023

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Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.

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Section: Related Workmentioning

confidence: 99%

All-in-one aerial image enhancement network for forest scenes

et al. 2023

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“…This method was able to record an accuracy rate that reached a value of 98.31%. On the other hand, another model based on transfer learning has been designed [24]. Its goal is to design a facial recognition model invariant to the activated age.…”

Section: Related Workmentioning

confidence: 99%

A combined method based on CNN architecture for variation-resistant facial recognition

Benradi,

Chater,

Lasfar

2023

Int. j. electr. comput. eng. syst. (Online)

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Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset.

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“…The count of layers in network equals network depth. The convolutional (Conv) layer width has proportional to count of filters it comprises [19][20][21][22]. The heights and widths of input images defined resolution.…”

Section: Feature Extraction: Efficientnet Modelmentioning

confidence: 99%

Automated ethnicity recognition using equilibrium optimizer with machine learning on facial images

et al. 2022

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In recent times, computer vision related face image analysis has gained significant attention in various applications namely biometrics, surveillance, security, data retrieval, informatics, etc. The main objective of the facial analysis is to extract facial soft biometrics like expression, identity, age, ethnicity, gender, etc. Of these, ethnicity recognition is considered a hot search topic, a major part of community with deep connections to many social and ecological concerns. The deep learning and machine learning methods is merit for effective ethnicity classification and recognition. This study develops a facial imaging based ethnicity recognition using equilibrium optimizer with machine learning (FIER-EOML) model. The goal of the FIER-EOML technique is to detect and classify different kinds of ethnicities on facial images. To accomplish this, the presented FIER-EOML technique applies an EfficientNet model to generate a set of feature vectors. For ethnicity recognition, the presented model uses long short-term memory method. To improve the recognition performance, the FIER-EOML technique utilizes EO algorithm for hyperparameter tuning process. The performance validation of the FIER-EOML technique is tested on BUPT-GLOBALFACE dataset and the results are examined under several measures. The comprehensive comparison study reported the enhanced performance of the FIER-EOML technique over other recent approaches.

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Bald eagle search optimization with deep transfer learning enabled age-invariant face recognition model

Cited by 21 publications

References 16 publications

All-in-one aerial image enhancement network for forest scenes

All-in-one aerial image enhancement network for forest scenes

A combined method based on CNN architecture for variation-resistant facial recognition

Automated ethnicity recognition using equilibrium optimizer with machine learning on facial images

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