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Object detection and age & gender estimation using deep learning: An overview

Manju AroraSahil SharmaMohd. Asif Khan

Abstract

In this research work, we explore the field of computer vision with a focus on creating a powerful and versatile framework. Our work leverages deep learning around important tasks such as object detection, age estimation, and gender estimation. By integrating the Mask R-CNN model for object detection and the Deep Face library for age and gender estimation, we propose a solution that transcends the boundaries of one objective. Our approach includes careful information before improving the quality of input images, which demonstrates the efficiency of our model. The Mask R-CNN model provides guidance in object detection by demonstrating the ability to identify and find objects in images. This is the basis for the next project, where we will turn it into age and gender estimation using the Deep Face library. Our test results show not only successful identification of people with reliable scores, but also accurate age and gender predictions. We discuss the complexity of our approach, acknowledge its strengths, and directly address issues that arise when using it.

Face recognition and analysisArtificial intelligenceEstimationComputer scienceDeep learningObject (grammar)Machine learningPsychologyEngineering
Citations
1
FWCI
0.25
field-weighted impact
References
13
Percentile
46%
vs. same field & year
References
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision · 2009 · 19,127 citations
Focal Loss for Dense Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence · 2018 · 9,349 citations
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Object detection and age & gender estimation using deep learning: An overview · Scinovex