Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition. Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset. In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques. There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.
翻译:深心神经网络(DNNS)已经成为机器学习的主导技术。 DNNS在包括图像分类、语音识别和面部识别在内的广泛任务方面表现最出色。 进化神经网络(CNNs)几乎在野生标签面部数据集(LFW)上的所有最佳性能方法中都使用了。 在本次演讲和配套文件中,我试图提供对最新工艺中所用深思熟虑技术的审查和总结。 此外,我还强调,需要更大和更具挑战性的公共数据集来为这些系统制定基准。 高精度(在出版时FaceNet为99.63%)和使用外部数据(谷歌脸网为100万张图像)表明,当前FLFW等面貌验证基准可能不够具有挑战性,也不足以提供当前技术所需的数据。 存在各种具有移动照片共享应用的组织,它们能够释放在移动设备上采集的面部图像的非常大规模和高度多样化的数据集。 这种“Imagenet for Face Resciencealence”的研究人员和“Imacial Investistrations for shall seargress for a swel