Ensemble Learning for Face Recognition in Suspect Identification using Cloud Environment

Abstract

Facial recognition technology finds applications in security, surveillance, and social media. Existing research explores the use of machine learning and deep learning for face recognition, emphasizing the need for improved accuracy. This paper proposes a system for suspect identification using facial recognition. The system leverages ensemble learning, combining machine learning and deep learning algorithms. It integrates seamlessly with OpenAI's advanced technologies and is supported by a robust cloud infrastructure. The proposed ensemble model's performance is compared to individual models like VGG-Face, Facenet, Facenet512, Deepface, DeepID, ArcFace, and SFace. The comparison uses multiple detectors and the Labelled Faces in the Wild  (LFW) dataset. The results show that the ensemble model offers the most efficient processing time across all sample sizes. In contrast, models like VGG-Face and DeepID exhibit a steeper increase in processing time, suggesting lower scalability. For instance, at a sample size of 50, the local test completes in 61.3 seconds, while the cloud API test takes 67.2 seconds. This highlights the faster processing speed of the local test across all sample sizes..