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@article{174438,
        author = {M.Hemantha Sai Krishna and V.V.Vidya Sagar and S.Ravi Venkata Kiran and P.Padmapriya Anushka and Anubhav},
        title = {IMAGE -BASED MISSING CHILD RECLAMATION MODEL USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4185-4190},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174438},
        abstract = {The growing number of missing children cases in India necessitates advanced technological interventions for efficient identification and recovery. This research presents a deep learning-based system that automates the process of missing child identification using facial recognition. The proposed approach integrates Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and alignment, and InceptionResNetV1 for feature extraction, ensuring robust and accurate identification. Additionally, an age estimation module is incorporated to account for facial changes over time, improving the system’s effectiveness in long-term missing cases. By leveraging deep learning techniques and real-time image matching, the system achieves a high accuracy of 99.4%, significantly surpassing traditional search methods. Furthermore, the integration of public image uploads and law enforcement databases enhances scalability, making it suitable for large-scale implementations. This AI-driven framework offers a reliable, real-time, and automated solution to improve the efficiency of missing child identification, ultimately aiding in faster and more successful recoveries.},
        keywords = {Missing child identification, Deep learning, Facial recognition, MTCNN, InceptionResNetV1, Age estimation, Convolutional Neural Networks (CNNs), Feature extraction.},
        month = {March},
        }