Abstract: The continuous progression of AI models for classification and Facial recognition has gained a lot of attention and importance these days and have immensely constituted in finding solutions for complex real-life issues. The Gender and Age Prediction is a Deep Learning application and falls under the area of Artificial Intelligence. Age and gender are considered as key attributes because they play a foundational role in social interactions. Estimating age and gender based on image(s) is considered as a crucial task in smart applications. The gender is expected to be classified into one of ‘MALE’ and ‘FEMALE’ however estimating age accurately using regression is considered as a monotonous job as even humans can’t accurately predict the age by looking intently at a person. However, still it can be determined whether a person is in their 20s or in their 30s. through an approach namely Age Prediction as grouping and classification task using Audience Dataset, it consists of images labelled with subsequent age groups [(0 – 2), (4– 6), (8 – 12), (15– 20), (25 – 32), (35 – 43), (45 – 53), (60 –100)] and gender labels ‘MALE’ and ‘FEMALE’. Convolution neural networks (CNN) are extensively being used for classification and facial recognition because of its exceptional efficiency in these tasks. “OpenCV” is an abbreviation for open-source computer vision. It's also a Machine Learning Library, capable of processing real-time image, video and supports the Deep Learning frameworks -Tensor Flow, Caffe, and PyTorch.
Keywords: Facial recognition, Convolution neural networks, Artificial Intelligence, Classification, Regression, OpenCV, Adience Dataset, Machine Learning Library, Tensor Flow, Caffe, and PyTorch.
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DOI:
10.17148/IJARCCE.2022.114126
