International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Automated Attendance System Using LBPH- BasedFace Recognition P. Madhavi1, K.M.D. Bhavani2, M.Geethanjali3, N. ManiSri4, P. Trinadh5, T. Sreenivasu6 1,2,3,4,5 - U.G. Students6 - Sr. Assistant Professor
Department of Electronics and communication Technology Sri Vasavi Engineering College (Autonomous), Tadepalligudem, Andhra Pradesh, India ----------------------------------------------------------------------------***-----------------------------------------------------------------------
recognition technology has been used to enhance customer service by providing personalized recommendations and improving security in stores.
ABSTRACT This technology uses biometric data to analyze and identify individuals based on their facial features, creating a unique "faceprint" that can be compared against a database of known faces. Automated attendance management systems are integral to various domains, facilitating streamlined tracking of attendance records while minimizing manual intervention. Initially, the system captures images or video streams of individuals entering the premises using cameras strategically placed at entry points. These images are then processed to extract facial features and generate unique face templates for individual. The extension for this implementation is, it maps more than two persons at a time. Individual student Images, labelled with H. T. No. The image captured with more than two students is compared with data base of the students if the student gets absent it need to mark the absent, if the student presents his/ her H.T. No should mark as Present. In the proposed system, we use the Haar cascade classifier to determine the presence or absence of faces and LBPH (Local binary pattern histogram) algorithm for face recognition.
In the rapidly evolving landscape of technology, facial recognition systems stand at the forefront, representing a profound intersection of artificial intelligence, computer vision, and biometric identification. These systems have garnered significant attention for their potential to revolutionize security, streamline processes, and personalize experiences. Yet, they also evoke complex discussions surrounding privacy, ethics, and societal implications. This comprehensive exploration delves into the multifaceted nature of facial recognition systems, examining their mechanisms, applications, challenges, and broader societal impact.
2. METHODOLOGY: 2.1 Local Binary Pattern Histogram (LBPH): LBPH, a widely utilized method in image processing, serves a robust technique for texture analysis and feature extraction. Its applications span various domains, including face recognition, texture classification, and object detection. By adeptly capturing local patterns within images, LBPH effectively describes texture, demonstrating resilience to lighting variations and other distortions. Its versatility renders it indispensable across both academic research and industrial contexts.
Keywords: Haarcascade classifier, LBPH (local binary pattern histogram) Algorithm.
1. INTRODUCTION Facial recognition technology has become increasingly prevalent in our society, with applications ranging from unlocking our smartphones to security surveillance in public spaces. This technology works by analyzing unique facial features of individuals, such as the distance between the eyes, the shape of the nose, and the contour of the jawline, to identify and verify their identity. While facial recognition systems offer many potential benefits, such as enhancing security and convenience, they also raise concerns about privacy, surveillance, and potential misuse. Debates continue about the ethical and legal implications of widespread facial recognition deployment, prompting discussions around regulation, accountability, and the protection ofindividual rights.
Fig 2.1.1 Face Recognition using lbph Algorithm
The impact of facial recognition technology has been farreaching, with applications in law enforcement, security, retail, and social media. On the positive side, facial recognition has enabled law enforcement agencies to quickly identify suspects and locate missing persons, leading to numerous successful investigations. In the retail sector, facial
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