Age and Gender prediction using Convolution, ResNet50 and Inception ResNetV2

Meghana, Avuthu Sai

doi:10.30534/ijatcse/2020/65922020

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…Avuthu Sai Meghana, Sudhakar S, Arumugam G, [2] and others who were a piece of this have driven their assessment towards the evaluation Mature enough and Direction using Convolution, ResNet50, and Beginning ResNetV2. In this paper, a CNN model was in like manner consolidated that isolates age and direction in the UTK Face dataset.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

M. R.

2023

IJRASET

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Humanity has made advancements in innovation pushing the boundaries of what was deemed impossible. With the help of cutting edge technology we can now determine a persons age and orientation by analyzing their image or video footage. This paperprovides an overview of the process including the techniques, strategies and algorithms involved. Unlike methods that treated these aspects separately this approach seamlessly integrates them all. It emphasizes the significance of this breakthrough. Explores its applications, in our daily lives. The main objective of this endeavor is to develop an age and orientation detector that utilizes Convolutional Neural Networks (CNN) and OpenCV to analyze features in images. Furthermore this paper showcases real world scenarios where this technology can be applied. Ranging from security services CCTV surveillance and law enforcement, to dating apps and matrimonial websites. Age and orientation estimations are determined based on image characteristics.

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Section: Related Workmentioning

confidence: 99%

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

M. R.

2023

IJRASET

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“…The investigator proposed the efficient Blockchain Technology Cross-domain Authentication Scheme and developed the trust model, and system architecture of Blockchain's Certification Agency [26] The BCCA Trust model is based on the Blockchain root CA that connects the consortium chain and the certificate hash value registered for secure and effective cross-domain authentication. In order to reinforce trust and transfer, Chen et al proposed a trust transfer system based in Blockchain.…”

Section: Related Workmentioning

confidence: 99%

Blockchain - Facilitated IoT Built Cleverer Home with Unrestricted Validation Arrangement

Satheesh¹

2020

IJATCSE

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The growing blockchain technology proves promising opportunities for enhancing the industrialized system and the Internet of Things (IoT) by given that idleness, storage space, and encryption for applications. Over the last few years, many new applications have started to emerge in industrial IoT, and blockchain technologies have attracted the attention of many researchers in academic and industrial. Decentralized security and privacy system based in Blockchain provide an enormous lift to the infrastructure of the IoT. In a smart home, Blockchain technology serves as the perfect gatekeeper for all communication packages. Authentication of identity is required before the terminal accesses the service to prevent access to unauthorized. The fact that trustworthy third parties are introduced destroys not only the independence and flexibility of the IoT system but also source problems such as a breakdown position and unilateral control dangers. A dynamic trust-right method was developed to advance the blockchain output and decrease the no. of authenticated communication in new blocks. A review and verification solutions based on the blockchain technology of the state of the art identity organizations. This article describes a decentralized model of identity verification that can guarantee autonomy and security initiative.

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“…One more method for training dense segmentation models was proposed in [18] based on ground truth labels allowing to effectively learn the coral segmentation. [19,20], speech recognition, bioinformatics, medical image analysis [21], natural language processing, object detection and recognition [22,23,24] were popular due to its ability to enhance training in terms of complexity and speed. These networks also attained better accuracy compared to other neural networks [25,26].…”

Section: Related Workmentioning

confidence: 99%

Evaluation of ResNet Network for Semantic Segmentation of Coral Reefs

Džakmić¹

2020

IJETER

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The Deep Residual Network (ResNet) learning model is known to achieve better accuracy and requiring shorter training time compared to other pre-trained learning models for image classifications and recognition. In this paper, the use of ResNet networks for semantic segmentation of coral reefs images was explored. Three ResNet networks (ResNet-18, ResNet-50, and ResNet-101) were evaluated and compared using 900 images as training dataset and 38 images as test dataset. The last three layers of the pre-trained ResNets were replaced with a set of layers that classified each pixel of the images into four classes: 'dead', 'alive', 'sand' and 'unknown'. A Softmax layer was introduced to reduce the imbalanced defects. Then, DeepLabv3+ employed the Atrous convolution to extract the features computed by applied CNN and segment the pixels of the object. ResNet-101 was shown to achieve better results compared to ResNet-18 and ResNet-50. Further analysis of the results implied that the class weightage assignment needs to be improved and more a larger training dataset should be acquired.

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Age and Gender prediction using Convolution, ResNet50 and Inception ResNetV2

Cited by 16 publications

References 12 publications

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

Blockchain - Facilitated IoT Built Cleverer Home with Unrestricted Validation Arrangement

Evaluation of ResNet Network for Semantic Segmentation of Coral Reefs

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