Smart Attendance Automation - Realtimemultiface Recognitionusing Deeplearning

Author(s)	Mr. Sanjay krishna B, Mr. Gokul E, Ms. Harini V, Ms. Harshavarthini S, Mr. Hemabal S, Mr. Harish R
Country	India
Abstract	Face detection and recognition are fundamental problems in computer vision, driven by their extensive use in surveillance, security, human–computer interaction, and multimedia applications. Conventional algorithm-based techniques often fail to perform reliably in real-world scenarios due to variations in illumination, pose, background complexity, and occlusions. To overcome these limitations, machine learning–based approaches have emerged as effective solutions by enabling systems to learn discriminative facial features directly from data. This research focuses on a machine learning framework that automatically extracts and preprocesses facial regions from video frames and employs a Haar Cascade–based detection model to achieve accurate and efficient multi-face detection in real-time environments. The approach demonstrates strong adaptability and reliability, making it suitable for large-scale video analysis without requiring pre-labeled data.
Keywords	Face Detection, Machine Learning, Haarcascade, Computer Vision, Video Analysis, Preprocessing, Real-Time Detection, Feature Extraction, Face Database
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-17
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.64990

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International Journal For Multidisciplinary Research