Face Recognition人脸识别,是基于人的脸部特征信息进行身份识别的一种生物识别技术。用摄像机或摄像头采集含有人脸的图像或视频流,并自动在图像中检测和跟踪人脸,进而对检测到的人脸进行脸部识别的一系列相关技术,通常也叫做人像识别、面部识别。
national conference on artificial intelligence, (2019)
We have presented Selective Refinement Network, a novel single shot face detector, which consists of two key modules, i.e., the step Classification and the step Regression
Cited by27BibtexViews159DOI
0
0
CVPR, (2017): 6738-6746
We propose the angular softmax loss for convolutional neural networks to learn discriminative face features with angular margin
Cited by1061BibtexViews168DOI
0
0
ieee international conference on automatic face gesture recognition, (2017)
The Faster R-convolutional neural networks is designed for generic object detection, it demonstrates impressive face detection performance when retrained on a suitable face detection training set
Cited by346BibtexViews103DOI
0
0
computer vision and pattern recognition, (2017)
We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stre...
Cited by90BibtexViews56DOI
0
0
CVPR, (2017)
The popularity of convolutional neural network in computer vision domain largely comes from its translation invariance property, which significantly reduces computation and model size compared to fully-connected neural networks
Cited by75BibtexViews97DOI
0
0
IEEE Signal Processing Letters, no. 10 (2016): 1499-1503
Experimental results demonstrate that our methods consistently outperform the state-of-the-art methods across several challenging benchmarks while keeping real time performance
Cited by1599BibtexViews131DOI
0
0
ECCV, pp.499-515, (2016)
By combining the center loss with the softmax loss to jointly supervise the learning of Convolutional neural networks, the discriminative power of the deeply learned features can be highly enhanced for robust face recognition
Cited by1534BibtexViews186DOI
0
0
ECCV, (2016)
We provide concrete measurement set for people to evaluate the model performance and provide, to the best of our knowledge, the largest training dataset to facilitate research in the area
Cited by634BibtexViews189DOI
0
0
computer vision and pattern recognition, (2016)
We wish to encourage the community to focusing on some inherent challenges of face detection – small scale, occlusion, and extreme poses. These factors are ubiquitous in many real world applications
Cited by611BibtexViews135DOI
0
0
ECCV, (2016)
In order to provide fair comparisons, our Convolutional Neural Network were fine tuned on CASIA subjects that are not included in Janus
Cited by277BibtexViews109DOI
0
0
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp.3456-3465, (2016)
We show that the back propagation algorithm used in training CNN can be naturally used in training CNN cascade
Cited by177BibtexViews82DOI
0
0
ECCV, (2016)
We proposed a new Supervised Transformer Network for face detection
Cited by106BibtexViews115DOI
0
0
Yunzhu Li, Benyuan Sun,Tianfu Wu,Yizhou Wang, Wen Gao 0001
ECCV, (2016)
We have presented a method of end-to-end integration of a ConvNet and a 3D model for face detection in the wild
Cited by100BibtexViews80DOI
0
0
WACV, (2016)
Though we only evaluated Facebook’s face detector, our results could apply to any social medium that uses automatic face recognition technology
Cited by15BibtexViews77DOI
0
0
IJCAI, pp.xxxvii-lii, (2016)
list-confcom
Cited by1BibtexViews63DOI
0
0
IEEE Conference on Computer Vision and Pattern Recognition, (2015)
We provide a method to directly learn an embedding into an Euclidean space for face verification
Cited by5373BibtexViews331DOI
0
0
IEEE Conference on Computer Vision and Pattern Recognition, (2015)
We present a convolutional neural networks cascade for fast face detection
Cited by928BibtexViews148DOI
1
0
CoRR, (2015)
This paper proposes two significantly deeper neural network architectures, coined DeepID3, for face recognition
Cited by641BibtexViews592DOI
0
0
ICMR, (2015)
We proposed a face detection method based on deep learning, called Deep Dense Face Detector
Cited by454BibtexViews167DOI
0
0
Brendan F. Klare,Ben Klein, Emma Taborsky,Austin Blanton, Jordan Cheney, Kristen Allen,Patrick Grother, Alan Mah,Mark Burge,Anil K. Jain
IEEE Conference on Computer Vision and Pattern Recognition, (2015)
This paper has introduced the Intelligence Advanced Research Projects Activity Janus Benchmark A dataset
Cited by453BibtexViews73DOI
0
0
No data, please see others