[Retracted] Object‐Based Image Retrieval Using the U‐Net‐Based Neural Network

Kumar, Sandeep; Jain, Arpit; Agarwal, Ambuj Kumar; Rani, Shilpa; Ghimire, A

doi:10.1155/2021/4395646

Cited by 63 publications

(10 citation statements)

References 40 publications

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“…The Histogram of Gradients (HOG) approach is providing features of the faces as well as the VGG16 and VGG19 deep learning pre training model used to extract important face features from celebrity photographs [10]. The current facial datasets must be enhanced with algorithms that allow face masked to recognise with low false-positive rates and overall high accuracy, without the user dataset, which must be produced by collecting new pictures for authentication [11]. A transfer learning model is used to provide and create fine-tuned state-of-the-art performance accuracy in learning models.…”

Section: Related Workmentioning

confidence: 99%

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Shukla¹,

Tiwari²

2023

Computer Systems Science and Engineering

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Corona virus (COVID-19) is once in a life time calamity that has resulted in thousands of deaths and security concerns. People are using face masks on a regular basis to protect themselves and to help reduce corona virus transmission. During the on-going coronavirus outbreak, one of the major priorities for researchers is to discover effective solution. As important parts of the face are obscured, face identification and verification becomes exceedingly difficult. The suggested method is a transfer learning using MobileNet V2 based technology that uses deep feature such as feature extraction and deep learning model, to identify the problem of face masked identification. In the first stage, we are applying face mask detector to identify the face mask. Then, the proposed approach is applying to the datasets from Canadian Institute for Advanced Research10 (CIFAR10), Modified National Institute of Standards and Technology Database (MNIST), Real World Masked Face Recognition Database (RMFRD), and Stimulated Masked Face Recognition Database (SMFRD). The proposed model is achieving recognition accuracy 99.82% with proposed dataset. This article employs the four pre-programmed models VGG16, VGG19, ResNet50 and ResNet101. To extract the deep features of faces with VGG16 is achieving 99.30% accuracy, VGG19 is achieving 99.54% accuracy, ResNet50 is achieving 78.70% accuracy and ResNet101 is achieving 98.64% accuracy with own dataset. The comparative analysis shows, that our proposed model performs better result in all four previous existing models. The fundamental contribution of this study is to monitor with face mask and without face mask to decreases the pace of corona virus and to detect persons using wearing face masks.

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Section: Related Workmentioning

confidence: 99%

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Shukla¹,

Tiwari²

2023

Computer Systems Science and Engineering

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“…Nonlinearity in thin layers is no longer an issue. Te state-of-the-art object recognition and semantic segmentation results are also achieved using MobileNetV2 as the backbone for feature extraction [29].…”

Section: Svmmentioning

confidence: 99%

An Efficient Approach of Face Detection and Prediction of Drowsiness Using SVM

Shukla

Tiwari²,

Jha

2023

Mathematical Problems in Engineering

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This article investigates an issue of road safety and a method for detecting drowsiness in images. More fatal accidents may be averted if fatigued drivers are using this technology accurately and the proposed models provide quick response by recognising the driver’s state of falling asleep. There are the following drowsiness models for depicting the possible eye state classifications as VGG16, VGG19, RESNET50, RESNET101 and MobileNetV2. The absence of a readily available and trustworthy eye dataset is perceived acutely in the realm of eye closure detection. On extracting the deep features of faces with VGG16, 98.68% accuracy has been achieved, VGG19 provides an accuracy of 98.74%, ResNet50 works with 65.69% accuracy, ResNet101 has achieved 95.77%, and MobileNetV2 is achieving 96.00% accuracy with the proposed dataset. The put forth model using the support vector machine (SVM) has been used to evaluate several models, and the present results in terms of loss function and accuracy have been obtained. In the proposed dataset, 99.85% accuracy in detecting facial expressions has been achieved. These experimental results show that the eye closure estimation has a higher accuracy and cheap processing cost, as well as the ability of the proposed framework for drowsiness.

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“…The methodology for developing predictive models in healthcare involves several steps, including problem definition, dataset preparation, pre-processing of data, selecting an apparent model, training the model, and performance evaluation of the model [4][5][6][7]. Factors that can affect the performance of predictive models include the size of the dataset and its quality, which is used to train the model, the choice of modeling algorithm, and the tuning of model parameters.…”

Section: Introductionmentioning

confidence: 99%

Depression Symptom Identification Through Acoustic Speech Analysis: A Transfer Learning Approach

Narayanrao,

Kohirker,

Preeth

et al. 2024

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In the field of mental health diagnostics, the acoustic characteristics of speech have been recognized as potent markers for the identification of depressive symptoms. This study harnesses the power of transfer learning (TL) to discern depression-related sentiments from speech. Acoustic features such as rhythm, pitch, and tone form the core of this analysis. The methodology unfolds in three distinct phases. Initially, a Multi-Layer Perceptron (MLP) network employing stochastic gradient descent is applied to the RAVDESS dataset, yielding an accuracy of 65%. This finding catalyzes the second phase, wherein a comprehensive hyperparameter optimization via grid search (GS) is conducted on the MLP Classifier. This step primarily focuses on detecting emotions commonly associated with depression, including neutrality, sadness, anger, fear, and disgust. The optimized MLP classifier indicates an improved accuracy of 71%. In the final phase, to enhance precision further, the same GS-based model, underpinned by TL principles, is applied to the TASS dataset. This application astonishingly achieves an accuracy of 99.80%, suggesting a high risk of depression. This comparative study establishes the proposed framework as a vanguard in the application of TL for depression prediction, showcasing a significant leap in accuracy over previous methodologies.

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[Retracted] Object‐Based Image Retrieval Using the U‐Net‐Based Neural Network

Cited by 63 publications

References 40 publications

Masked Face Recognition Using MobileNet V2 with Transfer Learning

Masked Face Recognition Using MobileNet V2 with Transfer Learning

An Efficient Approach of Face Detection and Prediction of Drowsiness Using SVM

Depression Symptom Identification Through Acoustic Speech Analysis: A Transfer Learning Approach

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