Identification Of Missing Child And Recovery System Using Multiclass SVM And Deep Learning

DOI

10.2991/978-94-6463-858-5_103How to use a DOI?

Keywords

Deep learning; Multiclass SVM; CNN; facial recognition; FGNET dataset; missing children; feature extraction

Abstract

The research presents a Deep Learning and Multiclass SVM-based approach to locate missing children. Using the FGNET dataset, a CNN model is trained to analyse new child photos uploaded by users, comparing them against a database of missing children. A CNN model trained on the FGNET dataset analyses uploaded images to detect facial matches with a missing child database. Facial features, including age, are extracted using SVM for classification. Authorized personnel can review the results for further investigation. Additionally, a Multiclass SVM classifier extracts facial attributes such as age and other features from the images. These extracted features are then input into the CNN to enhance identification. When a match is found, the result is saved for officials to access, enabling timely intervention. This approach offers a cost-effective alternative to traditional biometric methods.

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  - B. V. Chowdary
AU  - K. Likhitha Keerthi
AU  - D. Sandya
AU  - Ch. Sunny
AU  - M. Dilip  Reddy
PY  - 2025
DA  - 2025/11/04
TI  - Identification Of Missing Child And Recovery System Using Multiclass SVM And Deep Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1239
EP  - 1247
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_103
DO  - 10.2991/978-94-6463-858-5_103
ID  - Chowdary2025
ER  -