论文标题
SwiftFace：实时面部检测
SwiftFace: Real-Time Face Detection
论文作者
论文摘要
计算机视觉是一个人工智能领域，它以类似于人类的方式来训练计算机来解释视觉世界。由于技术方面的迅速发展以及足够大的培训数据集的可用性不断提高，在过去的十年中，计算机视觉中的主题已经急剧增长。其中，最有希望的领域之一是面部检测。每天在各种领域中使用；从移动应用程序和增强现实用于娱乐目的，到社会研究和安全摄像机；设计高性能模型以进行面部检测至关重要。最重要的是，随着面部检测技术的上述增长，精度和准确性不再是唯一的相关因素：对于实时面部检测，检测速度至关重要。 SwiftFace是一种新颖的深度学习模型，仅是为了成为快速面部检测模型。通过仅专注于检测面，SwiftFace的执行速度比当前最新面部检测模型快30％。可在https://github.com/leo7r/swiftface上找到代码
Computer vision is a field of artificial intelligence that trains computers to interpret the visual world in a way similar to that of humans. Due to the rapid advancements in technology and the increasing availability of sufficiently large training datasets, the topics within computer vision have experienced a steep growth in the last decade. Among them, one of the most promising fields is face detection. Being used daily in a wide variety of fields; from mobile apps and augmented reality for entertainment purposes, to social studies and security cameras; designing high-performance models for face detection is crucial. On top of that, with the aforementioned growth in face detection technologies, precision and accuracy are no longer the only relevant factors: for real-time face detection, speed of detection is essential. SwiftFace is a novel deep learning model created solely to be a fast face detection model. By focusing only on detecting faces, SwiftFace performs 30% faster than current state-of-the-art face detection models. Code available at https://github.com/leo7r/swiftface
