Detecting faces in images: a survey

TLDR

Face detection is crucial for vision‑based human‑computer interaction, yet faces vary widely in size, shape, color, and pose, making reliable localization challenging despite numerous existing algorithms. This survey categorizes and evaluates single‑image face detection algorithms to support fully automated vision systems. The authors review detection objectives and address data collection, evaluation metrics, and benchmarking practices. The survey identifies limitations of current algorithms and proposes several promising directions for future research.

Abstract

Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.