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Contents:
  1. Literaturhinweise der FG BIOSIG
  2. Dynamic Vision: From Images To Face Recognition : Shaogang Gong :
  3. VIDEO & IMAGE PROCESSING

We can describe the problem of face recognition as a supervised predictive modeling task trained on samples with inputs and outputs. In all tasks, the input is a photo that contains at least one face, most likely a detected face that may also have been aligned. The holistic approaches dominated the face recognition community in the s. In the early s, handcrafted local descriptors became popular, and the local feature learning approach were introduced in the late s.

Literaturhinweise der FG BIOSIG

Given the breakthrough of AlexNet in for the simpler problem of image classification, there was a flurry of research and publications in and on deep learning methods for face recognition. Capabilities quickly achieved near-human-level performance, then exceeded human-level performance on a standard face recognition dataset within a three year period, which is an astounding rate of improvement given the prior decades of effort.

DeepFace is a system based on deep convolutional neural networks described by Yaniv Taigman, et al. Our method reaches an accuracy of The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. The DeepID systems were among the first deep learning models to achieve better-than-human performance on the task, e. DeepID2 achieved FaceNet was described by Florian Schroff, et al.

Dynamic Vision: From Images To Face Recognition : Shaogang Gong :

FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Although these may be the key early milestones in the field of deep learning for computer vision, progress has continued, with much innovation focused on loss functions to effectively train the models.

02 - Databases of Images for Computer Vision Programming

In this post, you discovered the problem of face recognition and how deep learning methods can achieve superhuman performance. Do you have any questions? Ask your questions in the comments below and I will do my best to answer. It provides self-study tutorials on topics like: classification , object detection yolo and rcnn , face recognition vggface and facenet , data preparation and much moreā€¦. Click to learn more. Hi Jason, Nice post. Now i want to try face anti-spoofing.


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I need a training data set for that. I found few open source data set but those sites ask for signed agreement. Is there any open source data set available for face anti-spoofing without any agreement? Very interesting it was very informative. I have a question if you allow me.

Thanks for your reply, The concept I was implying is similar to the flipping coin, I will give you an example. Suppose you flip it three times and these flips are independent. Deep 3D face identification [J]. Rasmus S. Andersen, Anders U. EEG source imaging assists decoding in a face recognition task. Normface: l 2 hypersphere embedding for face verification. Aaron Nech, Ira Kemelmacher-Shlizerman. Rudd, Terrance E. Toward Open-Set Face Recognition.

Fares Jalled. Eilidh Noyes, Alice J. Face recognition assessments used in the study of super-recognisers. Karan Maheshwari, Nalini N. Shams, A. Tolba, S. Johannes Reschke, Armin Sehr. Sumit Shekhar, Vishal M. Yandong Guo, Lei Zhang.

Andrey V. Savchenko, Natalya S. Alexandr G. Rassadin, Alexey S. Gruzdev, Andrey V. Adversarial Discriminative Heterogeneous Face Recognition. Xihua Li. Le, Ioannis A. Rethinking feature discrimination and polymerization for large-scale recognition [J]. Garrett Bingham. Longitudinal Study of Child Face Recognition.

Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video. Qingxiang Feng, Yicong Zhou. Additive margin softmax for face verification. Face Recognition via Centralized Coordinate Learning. Face recognition for monitoring operator shift in railways. SeqFace: Make full use of sequence information for face recognition.

Anthony, Walter J. Nikita P. Face Recognition Techniques: A Survey. Mei Wang, Weihong Deng. Deep Face Recognition: A Survey. Face Recognition: Primates in the Wild. Surveillance Face Recognition Challenge. Fariborz Taherkhani, Nasser M. Nasrabadi, Jeremy Dawson.

Ziqing Feng, Qijun Zhao. Towards Interpretable Face Recognition. Lingfeng Zhang, Ioannis A. Alireza Sepas-Moghaddam, Mohammad A. Moeslund, Fernando Pereira. Low Resolution Face Recognition in theld. Large-scale Bisample Learning on ID vs. Spot Face Recognition. Md Ashraful Alam Milton.

VIDEO & IMAGE PROCESSING

I Gede Pasek Suta Wijaya. Daniel C.

Citations per year

Castro, Sebastian Nowozin. Git Loss for Deep Face Recognition. Weidi Xie, Andrew Zisserman. Multicolumn Networks for Face Recognition.


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The Devil of Face Recognition is in the Noise. Pairwise Relational Networks for Face Recognition. Patel, Ran He, Zhenan Sun. Vishwanath, S N Omkar. Jacek Komorowski, Przemyslaw Rokita. Face Recognition Based on Sequence of Images. Shiv Ram Dubey, Snehasis Mukherjee. Wesley Ramos dos Santos, Ivandre Paraboni. Personality facets recognition from text.

GhostVLAD for set-based face recognition. Vishal Agarwal. Deep Face Quality Assessment. Preliminary Studies on a Large Face Database.