Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
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TLDR
A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.About:
This article is published in Neurocomputing.The article was published on 2021-03-14 and is currently open access. It has received 353 citations till now. The article focuses on the topics: Deep learning & Feature extraction.read more
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Book ChapterDOI
Towards a reliable face recognition system.
TL;DR: There is a significant reliance by these methods on preprocessing for optimum performance of face detection and recognition models, particularly HOG, YOLO and MTCNN.
Journal ArticleDOI
Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
TL;DR: In this article , a global personalized head-related transfer function (HRTF) method based on anthropometric measurements and ear images was proposed, which greatly reduces the parameters and training cost of the model.
Breast Cancer Lesion Detection and Segmentation Based On Mask R-CNN
TL;DR: Wang et al. as discussed by the authors proposed an automatic breast mass segmentation method based on the Mask RCNN model of deep learning using detectron2, which achieved results with precision and F1 score 95.87 and 81.05 on INbreast dataset, respectively.
Journal ArticleDOI
Computer Vision in the Infrared Spectrum: Challenges and Approaches
TL;DR: In this article, cameras sensitive to the different infrared spectra can enhance the abilities of autonomous system, which is not the case for human visual perception, as shown in Figure 1.
Proceedings ArticleDOI
Comparison of Face Detection Algorithms on Mobile Devices
Yishi Guo,Burkhard C. Wünsche +1 more
TL;DR: This work compares four common face detection algorithms, Viola-Jones, HOG, MTCNN and MobileNet-SSD, for use in mobile robotics using different face data bases and shows that for a typical mobile configuration (Nvidia Jetson TX2) Mobile-NetSSD performed best with 90% detection accuracy for the AFW data set and a frame rate of almost 10 fps with GPU acceleration.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.