IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 5 2024
DOI: 10.56201/ijcsmt.v10.no5.2024.pg135.154

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Software Framework for Face Recognition System Using Amazon Rekognition

URBANUS, John Bille, Dr. Yusuf Musa Malgwi

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Abstract

This study presents the development and implementation of a software framework for a face recognition system using Amazon Rekognition, a cloud-based image and video analysis service provided by Amazon Web Services (AWS). The face recognition system is designed to offer a scalable, accurate, and efficient solution for identity verification and authentication, primarily targeting applications in security, access control, and user authentication systems. The framework integrates various AWS services, including Amazon S3 for image storage, Amazon Lambda for serverless computation, Amazon DynamoDB for database management, and API Gateway for interaction between the front-end application and backend services. During the implementation, Pycharm Integrated Development Environment (IDE) was used to develop user interface. The methodology employed in this study includes the collection of a dataset comprising 500 images for training and 200 images for testing. Images were gathered from primary and secondary sources, and preprocessing techniques such as face detection, normalization, and augmentation were applied to ensure the quality and consistency of the dataset. During the training phase, Amazon Rekognition’s IndexFaces API was used to extract unique facial features and store them for subsequent comparisons. In the testing phase, the SearchFacesByImage API was used to evaluate the system’s performance in identifying and verifying faces. The proposed framework was evaluated for performance metrics such as scalability, processing time, and accuracy, and compared with other face recognition models to determine its competitive advantages. By leveraging cloud-based technologies and serverless architectures, the system exhibits high scalability, minimal latency, and cost- efficiency, making it ideal for real-time, large-scale deployments. Additionally, the framework is flexible and can be integrated into various application domains, such a

keywords:

Amazon Rekognition, API Gateway, AWS, DynamoDB, Face Rekognition

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