论文标题
E-PRO：欧拉角和概率模型用于面部检测和识别
E-Pro: Euler Angle and Probabilistic Model for Face Detection and Recognition
论文作者
论文摘要
对面部外观的重视是人性的。通常，看起来不错就是感觉良好。同样，面部特征是这个星球上每个人所独有的，这意味着它是重要信息的来源。这项工作提出了一个名为E-PRO的框架，用于通过将面部图像作为输入来检测和识别面。 E-Pro在各个领域具有潜在应用，即出勤率，监视，人群监测，基于生物识别的身份验证等。在这里开发了E-Pro作为移动应用程序，旨在通过发现和识别来自图片的学生的面孔，以帮助讲师在教室中标记出勤。 E-Pro是使用Google Firebase面部识别API开发的，该API使用Euler角度和概率模型。 E-Pro已在库存图像上进行了测试，实验结果是有希望的。
It is human nature to give prime importance to facial appearances. Often, to look good is to feel good. Also, facial features are unique to every individual on this planet, which means it is a source of vital information. This work proposes a framework named E-Pro for the detection and recognition of faces by taking facial images as inputs. E-Pro has its potential application in various domains, namely attendance, surveillance, crowd monitoring, biometric-based authentication etc. E-Pro is developed here as a mobile application that aims to aid lecturers to mark attendance in a classroom by detecting and recognizing the faces of students from a picture clicked through the app. E-Pro has been developed using Google Firebase Face Recognition APIs, which uses Euler Angles, and Probabilistic Model. E-Pro has been tested on stock images and the experimental results are promising.
