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@article{199341,
        author = {Mohamed arif M and Ridwan U and J.Manickavasagam and S.Vikram balaji and M.Gughan raja and M. Kayathri devi},
        title = {Child vs Adult Detection Using MobileNetV2},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12699-12706},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199341},
        abstract = {This Paper Presents a real time system for Child vs Adult Detection using MobilenetV2, aimed at classifying individuals based on facial features. The system is designed for applications such as smart surveillance, parental control, and access management.
The proposed approach utilizes MobilenetV2, a lightweight convolutional neural network optimise for efficient computation. The system pipeline includes face detection, pre processsing, and classification. Detected facial images are resized and normalized before being fed into the trained model, which classifies them as child or adult. Transfer learning is applied to improve performance using pre-trained weights.
Experimental results show that the model achieves good accuracy with low computational cost, making it suitable for real-time deployment on resource-constrained devices. However, performance may be affected by lighting variations, occlusion, and dataset limitations. Future work focuses on improving robustness and extending the system to multi age classification.},
        keywords = {Child vs Adult Detection, MobilenrtV2, Deep Learning, Computer Vision, Face Detection, Image Classification, Real time Systems, Transfer Learning, Smart Surveillance.},
        month = {April},
        }