Real-Time Multi-Person Facial Expression Recognition Pipelines for CPU and Edge Deployment: A Systematic Review of Evidence and Performance Insights

Abstract

Real-time multi-person facial expression recognition (FER) is increasingly important for applications such as telemedicine, e-learning, workplace safety, and human–computer interaction, particularly in resource constrained CPU and edge-device environments. Despite rapid advances, selecting FER pipelines that balance accuracy, latency, and scalability without GPU acceleration remains a significant challenge. This paper presents a PRISMA-guided systematic review of 34 peer-reviewed studies published between 2015 and 2025, sourced from IEEE Xplore and ScienceDirect, with the aim of identifying the most practical FER pipelines for real-time multi-person FER on consumer-grade hardware with CPUs and integrated GPUs. The review evaluates four widely adopted approaches, including MTCNN, RetinaFace, MediaPipe Face Detection, and DeepFace, using reported metrics such as face detection accuracy, expression classification performance, latency, frames per second (FPS), multi-face robustness, and CPU feasibility. The analysis reveals that MediaPipe-based pipelines are consistently reported to achieve approximately 30–60 FPS on commodity CPUs, enabling stable multi-face tracking with low computational overhead. In contrast, RetinaFace demonstrates higher face detection accuracy, while DeepFace-based FER pipelines achieve higher expression classification accuracy when combined with robust face detection. However, both typically operate at approximately 5–10 FPS on CPUs without optimization, which limits their scalability in crowded or time-critical scenarios. The review also identifies inconsistent evaluation protocols and incomplete hardware reporting across studies, which hinder reproducibility and fair comparison. Overall, the findings position MediaPipe as the most practical solution for real-time multi-person FER on CPU and edge platforms and highlight the need for standardized evaluation frameworks to support future research and deployment.