Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing

Khan, Muhammad Zeeshan; Harous, Saad; Hassan, Saleet Ul; Khan, Muhammad Usman Ghani; Iqbal, Razi; Mumtaz, Shahid

doi:10.1109/access.2019.2918275

Cited by 135 publications

(57 citation statements)

References 23 publications

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“…• The standard (unit) softmax function, : ℝ → ℝ also known as annmmfyftygftgwstge3gfveg normalized exponential function, is defined by (6).…”

Section: Fcn and Votingmentioning

confidence: 99%

“…The benefits of employing UAVs include, mobility, low operating costs, and the possibility of computer vision with the aid of online edge computing. It is likely to achieve higher accuracies of detection and classification of outdoor objects using computer vision on edge devices enabled with deep learning CNN algorithms [6]. The overall framework of UAV deployment to enable visual inspection and analysis of insulators with the help of CNN algorithms is illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

et al. 2020

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A novel Fusion Convolutional Network (FCN) is proposed in this research for potential realtime monitoring of insulators using unmanned aerial vehicle (UAV) edge devices. Precise airborne imaging of outdoor objects, such as high voltage insulators, suffers from varied object resolution, cluttered backgrounds, unclear or contaminated surfaces, and illumination conditions. Accurate information about the insulator surface condition is essential and is of a high priority since insulator breakdown is a leading cause of electrical failure. A multi-modal information fusion (MMIF) system is developed during this research to analyze and classify possible contaminations present on the electrical insulators. A novel system, referred to as FCN, consists of a Convolutional Neural Network (CNN) and a binary Multilayer Neural Network (MNN) sub-classifier. While constructing the MMIF dataset for training and testing the novel FCN, the image classification output of the CNN is combined with the leakage current values (LCV) obtained as the classification output of MNN. Each sample of the MMIF dataset is, therefore, represented as a series of fusions. Later, sub-classifiers, of the FCN, are trained to identify the contamination types in the fusion series by implementing a voting system of sub-classifiers which is trained to identify a given class. As a result of the implementation of the proposed FCN, the classification accuracy increased by 8.4%, i.e., from 92% to 99.76%. To compare and benchmark the performance of proposed FCN, conventional classification algorithms are also implemented on the fusion of features that are extracted employing the wavelet transform and PCA methods. State-of-the-art CNN architectures are also discussed on account of their time consumption and memory usage. The conceptualization of a potential hardware implementation of the proposed FCN, on emerging edge devices, is also provided for completeness of the discussion. Pertinent outcomes of this research can be further extended to other potential applications of airborne imaging. INDEX TERMS Convolutional Neural Network, multi-modal information fusion, electrical insulators, unmanned aerial vehicle.

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“…• The standard (unit) softmax function, : ℝ → ℝ also known as annmmfyftygftgwstge3gfveg normalized exponential function, is defined by (6).…”

Section: Fcn and Votingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

et al. 2020

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“…The face data for machine learning, such as in [19], are usually transmitted to the edge and cloud servers without encryption, and thus, at the risk of being eavesdropped and abused. Many publications address the privacy preserving aspect of FR in edge and cloud computing scenario.…”

Section: A Fr In Cloud and Edge Networkmentioning

confidence: 99%

A Privacy-Preserving Learning Framework for Face Recognition in Edge and Cloud Networks

Wang

Nakachi

2020

IEEE Access

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Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service. Nevertheless, significant concerns on privacy are raised as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secured face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, a low-complexity encrypting algorithm is developed based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. Furthermore, it is proved that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity and avoid big data transmission between edge and cloud, a distributed learning framework is established, which extracts deeper features in an intermediate space, expanded according to the dictionaries from each device. Classification is performed in this new feature space to combat the noise and modeling error. Finally, the efficiency and effectiveness of the proposed framework is demonstrated through simulation results.

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“…The approaches presented to the International Symposium on Biomedical Imaging (ISBI) 2016 indicate this transition. The research contributors didn't apply conventional machine learning algorithms rather they all used a technique of deep learning: convolution neural networks (CNNs) [6] [86]. Zilong et al [9] review the few techniques for skin cancer detection using images.…”

Section: Introductionmentioning

confidence: 99%

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

et al. 2020

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Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.

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Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing

Cited by 135 publications

References 23 publications

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

Multi-Modal Data Fusion Using Deep Neural Network for Condition Monitoring of High Voltage Insulator

A Privacy-Preserving Learning Framework for Face Recognition in Edge and Cloud Networks

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

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