2,662 research outputs found
End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning
Sketch-based face recognition is an interesting task in vision and multimedia
research, yet it is quite challenging due to the great difference between face
photos and sketches. In this paper, we propose a novel approach for
photo-sketch generation, aiming to automatically transform face photos into
detail-preserving personal sketches. Unlike the traditional models synthesizing
sketches based on a dictionary of exemplars, we develop a fully convolutional
network to learn the end-to-end photo-sketch mapping. Our approach takes whole
face photos as inputs and directly generates the corresponding sketch images
with efficient inference and learning, in which the architecture are stacked by
only convolutional kernels of very small sizes. To well capture the person
identity during the photo-sketch transformation, we define our optimization
objective in the form of joint generative-discriminative minimization. In
particular, a discriminative regularization term is incorporated into the
photo-sketch generation, enhancing the discriminability of the generated person
sketches against other individuals. Extensive experiments on several standard
benchmarks suggest that our approach outperforms other state-of-the-art methods
in both photo-sketch generation and face sketch verification.Comment: 8 pages, 6 figures. Proceeding in ACM International Conference on
Multimedia Retrieval (ICMR), 201
High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
Synthesizing face sketches from real photos and its inverse have many
applications. However, photo/sketch synthesis remains a challenging problem due
to the fact that photo and sketch have different characteristics. In this work,
we consider this task as an image-to-image translation problem and explore the
recently popular generative models (GANs) to generate high-quality realistic
photos from sketches and sketches from photos. Recent GAN-based methods have
shown promising results on image-to-image translation problems and
photo-to-sketch synthesis in particular, however, they are known to have
limited abilities in generating high-resolution realistic images. To this end,
we propose a novel synthesis framework called Photo-Sketch Synthesis using
Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution
to high resolution images in an adversarial way. The hidden layers of the
generator are supervised to first generate lower resolution images followed by
implicit refinement in the network to generate higher resolution images.
Furthermore, since photo-sketch synthesis is a coupled/paired translation
problem, we leverage the pair information using CycleGAN framework. Both Image
Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to
demonstrate the superior performance of our framework in comparison to existing
state-of-the-art solutions. Code available at:
https://github.com/lidan1/PhotoSketchMAN.Comment: Accepted by 2018 13th IEEE International Conference on Automatic Face
& Gesture Recognition (FG 2018)(Oral
Image-Dependent Spatial Shape-Error Concealment
Existing spatial shape-error concealment techniques are broadly based upon either parametric curves that exploit geometric information concerning a shape's contour or object shape statistics using a combination of Markov random fields and maximum a posteriori estimation. Both categories are to some extent, able to mask errors caused by information loss, provided the shape is considered independently of the image/video. They palpably however, do not afford the best solution in applications where shape is used as metadata to describe image and video content. This paper presents a novel image-dependent spatial shape-error concealment (ISEC) algorithm that uses both image and shape information by employing the established rubber-band contour detecting function, with the novel enhancement of automatically determining the optimal width of the band to achieve superior error concealment. Experimental results corroborate both qualitatively and numerically, the enhanced performance of the new ISEC strategy compared with established techniques
Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning
Sketches obtained from eyewitness descriptions of criminals have proven to be useful in apprehending criminals, particularly when there is a lack of evidence. Automated methods to identify subjects depicted in sketches have been proposed in the literature, but their performance is still unsatisfactory when using software-generated sketches and when tested using extensive galleries with a large amount of subjects. Despite the success of deep learning in several applications including face recognition, little work has been done in applying it for face photograph-sketch recognition. This is mainly a consequence of the need to ensure robust training of deep networks by using a large number of images, yet limited quantities are publicly available. Moreover, most algorithms have not been designed to operate on software-generated face composite sketches which are used by numerous law enforcement agencies worldwide. This paper aims to tackle these issues with the following contributions: 1) a very deep convolutional neural network is utilised to determine the identity of a subject in a composite sketch by comparing it to face photographs and is trained by applying transfer learning to a state-of-the-art model pretrained for face photograph recognition; 2) a 3-D morphable model is used to synthesise both photographs and sketches to augment the available training data, an approach that is shown to significantly aid performance; and 3) the UoM-SGFS database is extended to contain twice the number of subjects, now having 1200 sketches of 600 subjects. An extensive evaluation of popular and stateof-the-art algorithms is also performed due to the lack of such information in the literature, where it is demonstrated that the proposed approach comprehensively outperforms state-of-the-art methods on all publicly available composite sketch datasets.peer-reviewe
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