The field of face analysis and recognition is rapidly advancing, with a focus on developing more efficient, accurate, and robust systems. Recent research has explored the use of edge GPUs to improve the performance of face detection and recognition tasks, resulting in significant gains in throughput and power consumption. Additionally, there is a growing interest in developing systems that can analyze facial expressive behaviors and recognize emotions in real-time. Noteworthy papers in this area include xTrace, which introduces a robust tool for facial expressive behavior analysis and predicting continuous values of dimensional emotions, and Edge-GPU Based Face Tracking, which suggests a combined hardware-software approach to optimize face detection and recognition systems on edge GPUs. These advancements have the potential to enable a wide range of applications, from improved video monitoring in public places to enhanced affective computing systems.