Vol. 2 No. 02 (2025): Smart Face Recognition with Database-Integrated Identity Verification

Abstract:

The abstract describes an integrated system developed to enhance security and surveillance measures through simultaneous face recognition and object detection. Utilizing advanced machine learning algorithms, the system efficiently processes video streams to identify individuals and detect potential threats or items of interest within a monitored environment. Face recognition is achieved using sophisticated convolutional neural networks that have been trained on extensive datasets to ensure accurate and reliable identification. Concurrently, object detection is conducted via a YOLO (You Only Look Once) model, known for its real-time processing capabilities, enabling the identification of various objects, including weapons, within the same frame as the face detection.This dual capability is particularly advantageous for applications requiring comprehensive monitoring solutions, such as public safety or secure access control in sensitive areas. The system provides real-time analytics and visualizations by annotating video frames with labels and bounding boxes, indicating the presence and identity of both individuals and detected objects. Designed with scalability in mind, the framework can be customized or expanded to accommodate specific security requirements, offering a robust toolset for modern surveillance needs. This integrated approach not only enhances the effectiveness of surveillance systems but also significantly contributes to proactive security management and incident prevention.

Keywords-face recognition, object detection, video surveillance, security systems, machine learning, convolutional neural networks, YOLO, real-time processing, public safety, secure access control, TensorFlow, OpenCV, deep learning, video analytics, surveillance technology, threat detection, computer vision, data visualization, scalability, proactive security management.