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@article{185596,
        author = {Noushan},
        title = {Missing child Identification System Using Convolutional Neural Network in Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2425-2432},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185596},
        abstract = {Numerous children are reported missing each year all across the world. A significant portion of missing kid cases involve untraceable children. In this research, we propose a revolutionary deep learning methodology that uses facial recognition to identify the child who was reported missing out of all the child photos that were available. The public can contribute images of dubious kids to a shared portal, along with landmarks and remarks. The photo will be instantly compared to the registered photos of the missing child. The picture that most closely fits the database of missing children will be selected once the input child photos has been classified.
In order to do this, a deep learning model is trained using the publically published facial image to accurately identify the absent youngster from the missing child picture database. The Convolutional Neural Network (CNN), a very effective deep learning technique for image-based applications, is used for facial recognition is used. The VGG-Face deep architecture, a trained CNN model, is used to extract face descriptors from the images. After the input child photograph has been classified, the picture that best matches the database of missing children will be chosen. 
In order to do this, a deep learning model is trained using the publically published facial image to accurately identify the absent youngster from the missing child picture database. The Convolutional Neural Network (CNN), a very effective deep learning technique for image-based applications, is utilized for face recognition. Face descriptors are extracted from the photos using a trained CNN model called the VGG-Face deep architecture.},
        keywords = {Convolutional Neural Network (CNN), VGG-Face deep architecture.},
        month = {October},
        }