The goal of this paper is face recognition - from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availabili...
Table1summarises the main deep face recognition surveys. The analysis presented by Wang et al. [46] is arguably the most comprehensive survey yet in the field. It provides a holistic overview of the broad topics of deep face recognition including the face recognition pipeline, face datasets,...
Deep convolutional neural networks (CNNs) are widely used in face recognition, because they can extract features with higher discrimination, which is the b
Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief...
The process of face recognition involves the determination of facial features in an image, by recognizing those features and comparing them to one of the many faces in the database. There are many algorithms capable of performing face recognition; such as: PCA, Discrete Cosine Transform, 3D reco...
Deep Learning: Face Recognition Deep Learning: Face Recognition English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 25m | 300 MB Face recognition is used for everything from automatically tagging pictures to unlocking cell phones. And with recent advancements in deep learning, the accuracy of...
Face recognition (FR) has been the prominent biometric technique for identity authentication and has been widely used in many areas, such as military, finance, public security and daily life. FR has been a long-standing research topic in the CVPR community. In the early 1990s, the study of ...
《A Discriminative Feature Learning Approach for Deep Face Recognition》 一种用于深度人脸识别的判别性特征学习方法 作者 Yandong Wen、Kaipeng Zhang、Zhifeng Li 和 Yu Qiao 来自深圳市计算机视觉与专利重点实验室、中国科学院深圳先进技术研究院和香港中文大学 ...
Towards Universal Representation Learning for Deep Face Recognition Abstract 识别自然环境下的人脸是非常困难的,因为它们会出现各种各样的变化。传统的方法要么使用来自目标域的特定标注的变化数据进行训练,要么通过引入未标记的目标变化数据来适应训练数据。相反,我们提出了一个通用的表示学习框架,它可以处理给定训练数据...
[acmi 2015]Image based Static Facial Expression Recognition with Multiple Deep Network Learning ABSTRACT 该文章作者为EmotiW2015比赛静态表情识别的亚军,采用的方法为cnn的级联,人脸检测方面也采用了当时3种算法的共同检测, 通过在FER2013数据库上进行模型预训练,并在SFEW2.0(比赛数据)上fine-tune,从而在比赛的验证...