Fast deep convolutional face detection in the wild exploiting hard sample mining[ J] . Big Data Research, 2017,3:1鄄鄄24.D. Triantafyllidou, P. Nousi, and A. Tefas, "Fast deep convo- lutional face detection in the wild exploiting hard sample min- ing," Big Data Research, 2017....
different information from different face parts. The network structures are specified in Fig.3. For instance, the filters at C1 of LNeto has 96 channels and the filter size in each channel is 11 11 3, as the input image xo contains three color channels. crop头像时可能会遭遇多目标检测问题,...
通常人脸识别里面先要对人脸图像进行检测和对齐,然后在相应的地方提取特征,但是在自然场景中,由于背景混乱,人脸检测和对齐会受到影响,进而影响特征提取和最后的识别效果。 这篇论文的主要思想是通过学习两个deep network来构建face attributes recognition的系统,其中第一个用来localization,第二个用来提取feature。 主要流程...
applied sciencesArticleFace Gender Recognition in the Wild: An ExtensivePerformance Comparison of Deep-Learned, Hand-Crafted, andFused Features with Deep and Traditional ModelsAlhanoof Althnian1, Nourah Aloboud2 , Norah Alkharashi 3, Faten Alduwaish4, Mead Alrshoud5and Heba Kurdi5,6, *??? ?
In recent years, deep networks has achieved outstanding performance in computer vision, especially in the field of face recognition. In terms of the performance for a face recognition model based on deep network, there are two main closely related factors: 1) the structure of the deep neural ne...
In this paper, we propose a new multilinear and multiview subspace learning method called Tensor Cross-view Quadratic Discriminant Analysis for face kinship verification in the wild. Most of the existing multilinear subspace learning methods straightforwardly focus on learning a single set of ...
in advance in a step called ‘enrolment process’. Here, the feature vectors/templates of the gallery subjects are generated. These features are then either stored with their corresponding labels or used to generate subject specific models. During the face recognition phase, the template of the ...
& Wolf, L. DeepFace: Closing the Gap to Human-Level Performance in Face Verification. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, 1701–1708, https://doi.org/10.1109/CVPR.2014.220 (IEEE, 201 4). Liu, Z., Luo, P., Wang, X. & Tang, X. Deep learning face ...
《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 ...
& Wolf, L. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2014 1701–1708 (IEEE, 2014). Huang, G. B., Ramesh, M., Berg, T. & Learned-Miller, E. Labeled faces in the Wild: ...