Master'sOpen Access

The efficiency comparison of facial recognition systems

2024
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Advisor: Doç. Dr. Sefer Kurnaz

Abstract (EN)

This article notes growing interest in facial recognition theories and algorithms. It is stated that various techniques such as local, holistic and hybrid approaches have been developed. These techniques are used to annotate facial images using only certain facial features or all facial features. In recent years, when studies in the field of image processing and computer vision have become widespread rapidly, computer vision applications have been developed and used in our daily lives in many fields such as agriculture, medicine, education, health and security. These facial recognition applications create data and knowledge bases by collecting facial information from users. One of the prominent applications is the facial recognition-based personnel control and tracking system developed to monitor personnel entry and exit in workplaces. This system offers a faster, effective and accurate solution instead of methods such as card or manual record keeping. The identities of the personnel are verified by detecting the faces of the personnel using cameras at the entrances and exits. In this way, personnel arrival and departure times and overtime information can be automatically tracked. This developed system offers an effective solution, especially in cases where traditional methods such as cards or fingerprints are challenging in terms of hygiene and health conditions. In this context, biometric systems are systems designed using special biometric features to determine the identities of individuals. Facial recognition systems, among these systems, identify people using facial features. This study contains detailed information about current facial recognition system applications. Keywords: Facial Recognition Systems, Security Systems, Biometric Systems, Person Identification, Efficiency

Author

Dr. Batuhan İyigel

How to Cite

Batuhan İyigel (Master Thesis). The efficiency comparison of facial recognition systems, 2024, Altınbaş University.

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