Journal ArticleDOI
Memetically Optimized MCWLD for Matching Sketches With Digital Face Images
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TLDR
An automated algorithm to extract discriminating information from local regions of both sketches and digital face images is presented and yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.Abstract:
One of the important cues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm to extract discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multiscale circular Weber's local descriptor. Further, an evolutionary memetic optimization algorithm is proposed to assign optimal weight to every local facial region to boost the identification performance. Since forensic sketches or digital face images can be of poor quality, a preprocessing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.read more
Citations
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Sketch image recognition using deep features
TL;DR: An end-to-end method has been proposed whereby the similarity score can be obtained directly when inputting a pair of sketch and photo-face images and this study investigates matching image pairs with more diversity than previous studies, considering such features as the presence of a beard, or haircut style, etc.
Journal ArticleDOI
Disguised heterogeneous face recognition using deep neighborhood difference relational network
Usman Cheema,Seungbin Moon +1 more
TL;DR: In this paper , the authors propose a deep neighborhood difference relational (DNDR) network and joint discrimination loss for disguised HFR, which calculates the difference relationships between cross-modality images in deep feature space.
Posted ContentDOI
A Logical Operator Oriented Face Retrieval Approach: How to Identify a Suspect Using Partial Photo Information from Different Persons?
Yiu-ming Cheung,Zhikai Hu +1 more
TL;DR: A new Logical Operation Oriented Face Retrieval (LOOFR) approach, in which the representations of partial information are learned in the first stage while the logical operations are processed in the second stage, and the target photo of a suspect can be retrieved.
Book ChapterDOI
Face Sketch-Image Recognition for Criminal Detection Using a GAN Architecture
TL;DR: In this article, the problem of matching a forensic sketch to a gallery of mugshot images is addressed by matching sketches with digital face images using a feature-based technique and compared against proposed generative adversarial networks (GANs).
Journal ArticleDOI
Unsupervised many-to-many image-to-image translation across multiple domains
TL;DR: A proposed many-to-many architecture with only one domain-shared encoder and several domain-specialized decoders to effectively and simultaneously translate images across multiple domains for unsupervised multi-domain image- to-image translation.
References
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TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Ten Lectures on Wavelets
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Journal ArticleDOI
Face Description with Local Binary Patterns: Application to Face Recognition
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
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