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Book ChapterDOI

IIIT-CFW: A Benchmark Database of Cartoon Faces in the Wild

TLDR
This database contains 8,928 annotated images of cartoon faces of 100 public figures and will be useful in conducting research on spectrum of problems associated with cartoon understanding.
Abstract
In this paper, we introduce the cartoon faces in the wild (IIIT-CFW) database and associated problems. This database contains 8,928 annotated images of cartoon faces of 100 public figures. It will be useful in conducting research on spectrum of problems associated with cartoon understanding. Note that to our knowledge, such realistic and large databases of cartoon faces are not available in the literature.

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Posted Content

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

TL;DR: This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances.
Proceedings Article

WebCaricature: a benchmark for caricature recognition

TL;DR: A new caricature dataset is built, with the objective to facilitate research in caricature recognition, and a framework for caricature face recognition is presented to make a thorough analyze of the challenges of caricature recognition.
Journal ArticleDOI

Universal Face Photo-Sketch Style Transfer via Multiview Domain Translation

TL;DR: A novel universal face photo-sketch style transfer method that does not need any image from the source domain for training and flexibly leverages a convolutional neural network representation with hand-crafted features in an optimal way is presented.
Proceedings ArticleDOI

Cartoon Face Recognition: A Benchmark Dataset

TL;DR: This work presents a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes, and proposes a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations.
Journal ArticleDOI

Unpaired photo-to-caricature translation on faces in the wild

TL;DR: Zheng et al. as mentioned in this paper designed a dual pathway model with one coarse discriminator and one fine discriminator to capture global structure with local statistics while translation, which can also be used for other high-level image-to-image translation tasks.
References
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Journal ArticleDOI

Face Photo-Sketch Synthesis and Recognition

TL;DR: A novel face photo-sketch synthesis and recognition method using a multiscale Markov Random Fields (MRF) model that allows effective matching between the two in face sketch recognition.
Journal ArticleDOI

A Survey of Face Recognition Techniques

TL;DR: A discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has been provided.
Proceedings ArticleDOI

A data-driven approach to cleaning large face datasets

TL;DR: An approach to building face datasets that starts with detecting faces in images returned from searches for public figures on the Internet, followed by discarding those not belonging to each queried person, and is releasing the FaceScrub dataset.
Journal ArticleDOI

A new ranking method for principal components analysis and its application to face image analysis

TL;DR: The experimental results have shown that the principal components selected by the separating hyperplanes allow robust reconstruction and interpretation of the data, as well as higher recognition rates using less linear features in situations where the differences between the sample groups are subtle and consequently most difficult for the standard and state-of-the-art PCA selection methods.
Journal ArticleDOI

Face Photo-Sketch Synthesis and Recognition

TL;DR: This paper is going to study a method for representing face which is based on the features which uses geometric relationship among the facial features like mouth, nose and eyes called Principal Component Analysis followed by Feed Forward Neural Network called PCA-NN.