CNN- Based Facial Analysis For Precise Age and Gender Recognition

DOI

10.2991/978-94-6463-858-5_123How to use a DOI?

Keywords

CNN (Convolution Neural Network); OpenCV; Face Detection; Datasets; Deep Learning; AI Models; Accuracy; Mean Absolute Error

Abstract

Artificial Intelligence is a mechanical discipline that has now become inseparable in the modern era. AI has made automation possible in different applications ranging from remote controls to autonomous vehicles, so that complicated sequences of tasks can now become feasible. This project discusses quick face and gender recognition through CNN and OpenCV technology. Hardware elements are usually employed by developers constructing AI models, with certain datasets assisting in training, tuning and validating the models for correct outputs. Deep Learning applications, especially in Visual Object Recognition such as Facial Detection, have been reported to have positive results based on previous studies. Inaccurate training datasets can, however, result in unpredictable outcomes if the model is not well-trained. With the help of CNN Training models and OpenCV Detection model, we can make predictions with outputs that are similar to predefined values. These models are useful in public areas, casinos, and restricted areas such as betting apps and streaming services to authenticate documents and safeguard children from explicit content. The emphasis is on successful implementation and not security issues.AI models aid decision-making making optimal use of resources and enhancing the quality of personal services. Use CNNs to automatically extract features and train on a large labelled dataset. Crossing CNN and OpenCV technologies enhance the detection speed and accuracy allowing false positives to be minimized. Proper preparation and the addition of the dataset for every type of condition (lighting, and angles) are the primary key to achieving consistent and meaningful results. The MAE in age classification for the test set is 7.2728. The model for gender classification had an accuracy of 89% on the test set.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Afreen Subuhi
AU  - J. Sathwik
AU  - Sai Kumar
AU  - Tanuja
PY  - 2025
DA  - 2025/11/04
TI  - CNN- Based Facial Analysis For Precise Age and Gender Recognition
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1496
EP  - 1507
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_123
DO  - 10.2991/978-94-6463-858-5_123
ID  - Subuhi2025
ER  -