International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
FACE VISION : Real Time Age and Gender Prediction Abhishek Nimbalkar1, Navnath B. Pokale2, Anjali Vidhate3 , Shweta Bagul4,Aakashkumar Patil 5 2 Assistant Professor TSSM BSCOER College of Engineering Narhe
1,3,4,5Undergraduate Students, Department of Computer Engineering, TSSM BSCOER, Savitribai Phule Pune
University, Pune 411041, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This study introduces a robust system for age
Gender and age are two characteristics that are essential to our social lives. According to recent study, the effectiveness of age rating based on face images has significantly improved due to the deep learning of aging characteristics from large amounts of data. With the advent of social networks and social media, automatic age and gender detection has become crucial for an increasing variety of applications[1].
and gender detection from facial images using a fine-tuned convolutional neural network (CNN). The proposed approach leverages advanced deep learning techniques to extract comprehensive features and perform precise classification tasks. The system's application extends to social media platforms, where it plays a crucial role in delivering targeted advertisements and marketing campaigns, thereby maximizing their outreach and effectiveness.
In the past, many methods were proposed to begin face recognition such as the eigenface analysis, Fisher face analysis, independent component analysis (ICA), tensor face analysis, and their extensions. Furthermore, gender identification will shield users from cyberbullying and stop shady users from making fictitious social media profiles. Furthermore, social networks will be able to show content that is likely to appeal to that particular group thanks to an automated age and gender forecast [1].
The rapid evolution of face recognition technology necessitates continuous exploration and refinement to achieve higher accuracy and applicability across various domains. Model: ‘gender_net.caffemodel’. This is the pretrained model file for the gender classification model. By integrating cutting-edge deep learning methods with practical use cases like social media marketing, this research contributes to advancing facial recognition technologies. The findings from this study facilitate enhanced user experiences and enable more tailored content delivery strategies, thus driving innovation in digital marketing and personalized advertising.
Our study contributes to the ongoing efforts to refine facial recognition technologies, with a particular focus on age and gender prediction. By harnessing pretrained models and a carefully curated dataset, we aim to develop a highly accurate and efficient model that can be deployed across various domains, including security, personalized content delivery and marketing strategies.
This study introduces a robust system for age and gender detection from facial images using a fine-tuned convolutional neural network (CNN Model: ‘gender_net.caffemodel’. This is the pre-trained model file for the gender classification model.
In this paper, we attempt to propose a paper validator based on automatic age and gender distribution using CNN to identify gender and age diversity in user images and evaluate it using a dataset of real-life images.
This system comprises essential stages such as face detection, alignment, and feature extraction, leading to accurate predictions of age and gender. Experimental results demonstrate consistently high accuracy rates, underscoring the effectiveness and practicality of the proposed approach for real-world applications.
2. RELATED WORK Researchers have been trying to determine and estimate age and gender for a long time. They were able to do this by applying a range of deep learning strategies intended to improve the accuracy and precision of determining age and gender from human images. Many deep learning techniques were used for face identification, face recognition, age estimate, and gender detection and recognition. With the use of neural networks, these techniques examine patterns, textures, and facial traits to produce predictions that are more precise and detailed. These models have become more complex as technology develops, opening up new possibilities for applications in fields like healthcare, security systems, and targeted marketing.
Key Words: CNN(Convolution neural network), Age Prediction, Gender Prediction, Biometric System, Face Detection
1.INTRODUCTION Human gender and age are regarded as crucial biometric characteristics for identifying individuals. The method of detecting a person's gender and age based on facial recognition in an image is known as age and gender prediction. Real-time facial characteristic recognition is a very interesting area for future research.
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