A survey on heterogeneous face recognition
Reads0
Chats0
TLDR
This survey provides a comprehensive review of established techniques and recent developments in HFR, and offers a detailed account of datasets and benchmarks commonly used for evaluation.About:
This article is published in Image and Vision Computing.The article was published on 2016-12-01 and is currently open access. It has received 114 citations till now.read more
Citations
More filters
Book ChapterDOI
Cross-Domain Face Recognition Using Dictionary Learning
TL;DR: This paper proposes a dictionary learning based method to learn the common subspace in order to reduce the gap between domains in cross-domain face recognition.
Proceedings ArticleDOI
Cross-modal face matching: Tackling visual abstraction using fine-grained attributes
Yichuan Hu,Ke Li,Honggang Zhang +2 more
TL;DR: This paper proposes a simple yet effective geometry-based attribute classifier to detect fine-grained attributes at part-level, and demonstrates how meaningful facial regions can be reliably detected to enable localized feature extraction and attribute detection.
Proceedings ArticleDOI
Face Recognition of Intelligent Building based on Super-Resolution Reconstruction of Visual Image
TL;DR: Wang et al. as discussed by the authors focused on face details, through face super-resolution reconstruction technology for face recognition, to provide more information for many real scene applications such as face recognition; the proposed method has high recognition rate and speed.
Journal ArticleDOI
Towards creating a reference based self-learning model for improving human machine interaction
TL;DR: A reference based self-learning model is proposed, which can learn classification on new data from its previous trained models, which achieves an accuracy of around 90% using reference based learning.
Posted Content
LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition
TL;DR: In this paper, a spectral conditional attention module was introduced to reduce the domain gap between NIR and VIS data and then improved the performance of NIR-VIS heterogeneous face recognition on various databases including LAMP-HQ.
References
More filters
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.
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.