A two-stage clustering-based distributed framework for large-scale face identification

Face recognition poses challenges in accuracy, memory efficiency, and computational complexity. This study proposes a two-stage, three-module approach: Subnetwork modules, Cluster-Finder unit, and Final-Decision module. Unlike random distribution methods, our approach employs clustering for distribution. Each subnetwork, a supervised deep neural network, is trained with cluster-specific data. The Cluster-Finder unit compares test data similarity with each subnetwork’s representative. The Final-Decision module selects the best class. Results indicate superior accuracy, recall, and F1 score compared to competitive methods. The approach is faster and more accurate than non-distribution methods, with comparable speed and higher accuracy than random distribution methods. Experiments on VGGFace2, MS-Celeb-1M, and Glint360K datasets confirm both superior performance and scalability. The proposed method, using KMeans for distribution, outperforms Softmax Dissection and Dynamic Active Class Selection. It simplifies training without additional manipulations, offering efficiency over methodologies like Softmax Dissection and ArcFace parallelization. In conclusion, this study focuses on pre-processing and post-processing without added training complexity. A divide-and-conquer approach addresses accuracy and efficiency challenges. In this study, various sources leading to errors in face recognition systems have been examined. These sources include: imprecise features, overfitting, challenging classes, distribution issues, and decision-making complexities. Various classification scenarios are explored, including non-distributed and models with random and intelligent distributions. Inaccurate features uniformly impact all scenarios, with overfitting posing the greatest challenge in non-distributed scenarios. Challenging classes are better distinguished in intelligent distribution scenarios. Inappropriate distribution has less impact in intelligent scenarios, and decision-making challenges exist in both distributions