人脸检测
计算机科学
对象类检测
人工智能
三维人脸识别
计算机视觉
面部识别系统
水准点(测量)
面子(社会学概念)
不变(物理)
人脸识别大挑战
模式识别(心理学)
钥匙(锁)
社会科学
物理
计算机安全
大地测量学
社会学
数学物理
地理
作者
Shubh Lakshmi Agrwal,Sudheer Kumar Sharma,Vibhor Kant
标识
DOI:10.1109/icct56969.2023.10076222
摘要
With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research.
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