论文标题
使用复杂的神经网络实时面部识别
Real Time Face Recognition Using Convoluted Neural Networks
论文作者
论文摘要
面部识别是识别使用面部的人的过程之一，它具有各种应用，例如身份验证系统，监视系统和执法部门。事实证明，卷积神经网络最适合面部识别。使用Core-ML API检测面并通过Coreml模型处理提取的面孔，该模型经过训练以识别特定人员。数据集的创建是通过将人的面部视频转换为数百个人的图像来完成的，这进一步用于培训和验证模型以提供准确的实时结果。
Face Recognition is one of the process of identifying people using their face, it has various applications like authentication systems, surveillance systems and law enforcement. Convolutional Neural Networks are proved to be best for facial recognition. Detecting faces using core-ml api and processing the extracted face through a coreML model, which is trained to recognize specific persons. The creation of dataset is done by converting face videos of the persons to be recognized into Hundreds of images of person, which is further used for training and validation of the model to provide accurate real-time results.
