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
Proceedings ArticleDOI
New model for matching the sketch image with corresponding real image
TL;DR: A new model that takes a sketch image as a input and retrieves a real image as an output is proposed that allows to build new faces with different expressions and signs of aging.
Journal ArticleDOI
Recent Advances in Infrared Face Analysis and Recognition with Deep Learning
TL;DR: In this paper , the authors provide an updated overview of deep infrared (IR) approaches in face recognition and analysis and identify some weaknesses of current infrared FR approaches as well as many future research directions to address the IR FR limitations.
Journal ArticleDOI
Face Recognition in Cross-spectral Environment using Deep Learning
Sana Khan,Zuber Farooqui +1 more
TL;DR: A 26-layered deep residual network is extracted discriminative features from the face images and learn the common feature of the subject in the cross-spectral for the matching of face images capture in the nearinfrared spectrum to face images of the visible spectrum.
Journal ArticleDOI
Person Re-Identification With Character-Illustration-Style Image and Normal Photo
TL;DR: Zhang et al. as mentioned in this paper proposed a semi-coupled mapping and discriminant dictionary learning (SMD2L) method to handle the problem of pedestrian retrieval with character-illustration-style images and normal photos.
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.