Experiments on Deep Face Recognition Using Partial Faces

Elmahmudi, Ali; Ugail, Hassan

doi:10.1109/cw.2018.00071

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…The term CNN derived from Convolutional Neural Networks is the most globally used by researchers and scientist of machine vision field for the application of image classification and segmentation of images. This algorithm has been acknowledged greatly and largely utilized in several application areas like character recognition, handwriting recognition, image processing and face recognition, [1,2] and also in animal/skin/ plant disease identification [3]. The main use of Conv-Net is the ability for identification and collection of variety ofrich parameters that differentiates the features at every level of processing.…”

Section: Basic Foundationsmentioning

confidence: 99%

FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

Pabitha¹,

Vanathi²

2022

Computer Systems Science and Engineering

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Skin is the largest body organ that is prone to the environment most specifically. Therefore the skin is susceptible to many damages, including burn damage. Burns can endanger life and are linked to high morbidity and mortality rates. Effective diagnosis with the help of accurate burn zone and wound depth evaluation is important for clinical efficacy. The following characteristics are associated with the skin burn wound, such as healing, infection, painand stress and keloid formation. Tissue regeneration also takes a significant amount of time for formation while considering skin healing after a burn injury. Deep neural networks can automatically assist in the extraction of features from a burn image. In our approach to burn wound analysis and regeneration of the tissue of the skin burn wound, we use the Faster RCNN (Regional Convolutional Neural Network), which is based on their severity of the burn wound. The success rates of skin cure for burning injuries can be dramatically increased with the use of different skin replacements. Our objective is to analyze different deep learning techniques that may help to analyze and classify burn wounds in a superficial, partial and complete thickness, while treating burn wounds more accurately. The application of Faster RCNN effectively classifies wound without first degree, second and third degree confusion, thus providing a suitable solution to burning wounds. The advancement in the field of profound training offers an important path in the field of the processing and burning of trauma.

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Section: Basic Foundationsmentioning

confidence: 99%

FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

Pabitha¹,

Vanathi²

2022

Computer Systems Science and Engineering

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“…Deep learning has been applied in the medical field to address various problems such as face recognition [5][6][7], effective classification of skin burns [8][9][10][11][12], and cancer diagnosis [13][14][15], as well as in financial fraud detection [16,17]. Interestingly, a similar approach was adopted recently to discriminate between blood-smear images that include the Plasmodium parasite and those that do not.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It has been applied successfully to various classification problems related to healthcare issues. [10,12]. SVM works by iteratively segregating inputs into two by finding an optimum separating hyperplane therefore producing a maximum separating distance (margin) between them.…”

Section: Classificationmentioning

confidence: 99%

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Abubakar

Ajuji

Yahya

2021

ASI

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Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Six different features—VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models—were extracted. Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature.

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“…The use of Convolutional Neural Networks (CNN) for classification tasks has widely been adopted in different application domains such as face recognition [13,14] and disease detection [15]. Their adoption was due to their capability to capture rich generic discriminatory features at different levels.…”

Section: Literaturementioning

confidence: 99%

Burns Depth Assessment Using Deep Learning Features

et al. 2020

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Purpose Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique. Method In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. Results The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively. Conclusion The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.

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Experiments on Deep Face Recognition Using Partial Faces

Cited by 19 publications

References 24 publications

FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

FASTER–RCNN for Skin Burn Analysis and Tissue Regeneration

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Burns Depth Assessment Using Deep Learning Features

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