Ensemble Learning for Face Recognition in Suspect Identification using Cloud Environment

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 by integrating seamlessly with OpenAI’s advanced technologies and is supported by a robust cloud infrastructure. The comparison of the proposed ensemble model to individual models like VGG-Face, Facenet, Facenet5l2, Deepface, DeepID, ArcFace, and SFace 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. FaceNet, VGG-Face, and ArcFace models are chosen in ensemble model where in all of them have accuracy above 95% in every face detector test. Facenet512 model has 98.4% among the selected ensemble model whereas ensemble of these models shows 98.8 accuracy.