VGGFace2 Dataset for Face Recognition The dataset contains 3.31 million images of 9131 subjects (identities), with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians).

2019年6月4日 · In this tutorial, you will discover how to develop face recognition systems for face identification and verification using the VGGFace2 deep learning model. After completing this tutorial, you will know:

The VGG-Face CNN descriptors are computed using our CNN implementation based on the VGG-Very-Deep-16 CNN architecture as described in [1] and are evaluated on the Labeled Faces in the Wild [2] and the YouTube Faces [3] dataset.

Live Face Identification with pre-trained VGGFace2 model. How to Perform Face Recognition With VGGFace2 in Keras. An extremely small FaceRecog project for extreme beginners, and a few thoughts on the future

VGGFace 是牛津大学视觉组于2015年发表， VGGNet 也是他们提出的，基于VGGNet的人脸识别，Deep Face Recognition，官网 主要思想. 目标：构建最少的人为干预大规模人脸数据集，使训练的卷积神经网络进行人脸识别，可以与谷歌和Facebook等互联网巨头竞争。

The VGG Face dataset is face identity recognition dataset that consists of 2,622 identities. It contains over 2.6 million images.

2017年10月23日 · In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians).

2019年10月16日 · Face recognition in OpenCv, Tensorflow-keras with Dlib face detector and Vgg face model.

2019年12月23日 · From creating attendance system as basic need to use Face Recognition as security in Airports, criminal detection, face tracking, forensic, public places etc.

2017年10月23日 · In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians).