International Journal of
Advanced Multidisciplinary Application

Volume 3, Issue 8

• Author(s)G.G.Rajput, Durga M Ganjagol and Pavitra Muttur
• Affiliation1,2,,3Department of Computer Science Karnataka State Akkamahadevi Women University Vijayapura, Karnataka, India
• Page No.15-25
• Volume, Issue & YearVolume 3 Issue 8,Aug-2026
• Published On11/09/2026
• JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
• ISSN No.3048-9350

Abstract

Deep learning has greatly enhanced the performance of numerous recognition systems. and in particular facial recognition. However, achieving robust real time recognition under diverse facial appearances and integrating Internet of Things (IoT) for real time application is challenging. In this paper we propose Multi task Cascaded Convolutional Neural Network (MTCNN) deep learning model for robust facial recognition and integrating the proposed method in IoT environment for real time face recognition. MTCNN is used to precisely localize and align facial regions, while FaceNet model is used to generate compact facial embeddings that are subsequently classified using a Support Vector Machine (SVM) classifier. Experiments were carried out on images from LFW facial image dataset. The proposed recognition framework achieved an accuracy of 97.84%, demonstrating reliable facial identity classification on the LFW dataset. The proposed system is integrated with ESP32-CAM and Arduino to enable IoT-based remote monitoring and secure access control. The proposed framework offers an accurate, efficient, and practical solution for smart security, attendance management, and real-time surveillance.

Keywords: Face Recognition, Deep Learning, MTCNN, FaceNet, Support Vector Machine, Internet of Things.

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