Gravitational search-based optimal deep neural network for occluded face recognition system in videos

Shirley, C.; Mohan, Nitinder; Chitra, B.

doi:10.1007/s11045-020-00733-0

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…Once the feature extraction process is completed, the selected features are fed to the classification phase input [39].…”

Section: Image Classificationmentioning

confidence: 99%

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

Madhumalini

Devi

2021

Preprint

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Glaucoma is a retinal disease that damages the eye's optic nerve, frequently causing an irreversible loss of vision. However, the accurate diagnosis of this disease is difficult but early-stage diagnosis may cure this retinal disease. The objective of this research is to diagnose glaucoma disease in the top of the eye's optical nerve. The proposed approach detects glaucoma via four major steps namely Data enhancement phase, segmentation phase, feature extraction phase, and classification phase by the fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) classifier. The proposed classifier is used for the exact classification of glaucoma infected images and normal images. Here, the proposed approach utilizes the statistical, textural, and vessel features from the segmented output. Also, the proposed FGSO algorithm is used for testing the deep neural network. From the experimental results, it is observed that the proposed glaucoma detection has obtained a sensitivity of 99.64%, a specificity of 97.84%, and an accuracy of 98.75% that outperforms other state-of-art methods.

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“…Once the feature extraction process is completed, the selected features are fed to the classification phase input [39].…”

Section: Image Classificationmentioning

confidence: 99%

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

Madhumalini

Devi

2021

Preprint

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“…Recently, face recognition techniques have achieved high performance on some public databases [4,5]. However, in most real-life applications, face recognition accuracy may degrade by inevitable causes, such as occlusion, facial expressions, poses, and illumination [4,6]. Face occlusion is a common challenging problem studied extensively in recent years [4,[7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Feature Subset Selection for Occluded Face Recognition

Alsaedi¹,

Jaha²

2022

Intelligent Automation &Amp; Soft Computing

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Accurate recognition of person identity is a critical task in civil society for various application and different needs. There are different well-established biometric modalities that can be used for recognition purposes such as face, voice, fingerprint, iris, etc. Recently, face images have been widely used for person recognition, since the human face is the most natural and user-friendly recognition method. However, in real-life applications, some factors may degrade the recognition performance, such as partial face occlusion, poses, illumination conditions, facial expressions, etc. In this paper, we propose two dynamic feature subset selection (DFSS) methods to achieve better recognition for occluded faces. The proposed DFSS methods are based on resilient algorithms attempting to minimize the negative influence of confusing and low-quality features extracted from occluded areas by either exclusion or weight reduction. Principal Component Analysis and Gabor filtering based approaches are initially used for face feature extraction, then the proposed DFSS methods are dynamically enforced. This is leading to more effective feature representation and an improved recognition performance. To validate their effectiveness, multiple experiments are conducted and the performance of different methods is compared. The experimental work is carried out using AR database and Extended Yale Face Database B. The obtained results of face identification and verification show that both proposed DFSS methods outperform the standard (static) use of the whole number of features and the equal feature weights.

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POC-net: pelican optimization-based convolutional neural network for recognizing large pose variation from video

Jayabharathi,

Suresh

2023

Neural Comput & Applic

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Gravitational search-based optimal deep neural network for occluded face recognition system in videos

Cited by 9 publications

References 35 publications

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

Dynamic Feature Subset Selection for Occluded Face Recognition

POC-net: pelican optimization-based convolutional neural network for recognizing large pose variation from video

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