48,993 research outputs found
Privacy Protection Performance of De-identified Face Images with and without Background
Li Meng, 'Privacy Protection Performance of De-identified Face Images with and without Background', paper presented at the 39th International Information and Communication Technology (ICT) Convention. Grand Hotel Adriatic Congress Centre and Admiral Hotel, Opatija, Croatia, May 30 - June 3, 2016.This paper presents an approach to blending a de-identified face region with its original background, for the purpose of completing the process of face de-identification. The re-identification risk of the de-identified FERET face images has been evaluated for the k-Diff-furthest face de-identification method, using several face recognition benchmark methods including PCA, LBP, HOG and LPQ. The experimental results show that the k-Diff-furthest face de-identification delivers high privacy protection within the face region while blending the de-identified face region with its original background may significantly increases the re-identification risk, indicating that de-identification must also be applied to image areas beyond the face region
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Fast Face-swap Using Convolutional Neural Networks
We consider the problem of face swapping in images, where an input identity
is transformed into a target identity while preserving pose, facial expression,
and lighting. To perform this mapping, we use convolutional neural networks
trained to capture the appearance of the target identity from an unstructured
collection of his/her photographs.This approach is enabled by framing the face
swapping problem in terms of style transfer, where the goal is to render an
image in the style of another one. Building on recent advances in this area, we
devise a new loss function that enables the network to produce highly
photorealistic results. By combining neural networks with simple pre- and
post-processing steps, we aim at making face swap work in real-time with no
input from the user
Comparing Evolutionary Operators, Search Spaces, and Evolutionary Algorithms in the Construction of Facial Composites
Facial composite construction is one of the most successful applications of interactive evolutionary computation.
In spite of this, previous work in the area of composite construction has not investigated the
algorithm design options in detail. We address this issue with four experiments. In the first experiment a
sorting task is used to identify the 12 most salient dimensions of a 30-dimensional search space. In the second
experiment the performances of two mutation and two recombination operators for interactive genetic
algorithms are compared. In the third experiment three search spaces are compared: a 30-dimensional
search space, a mathematically reduced 12-dimensional search space, and a 12-dimensional search space
formed from the 12 most salient dimensions. Finally, we compare the performances of an interactive
genetic algorithm to interactive differential evolution. Our results show that the facial composite construction
process is remarkably robust to the choice of evolutionary operator(s), the dimensionality of the search
space, and the choice of interactive evolutionary algorithm. We attribute this to the imprecise nature of human
face perception and differences between the participants in how they interact with the algorithms.
Povzetek: Kompozitna gradnja obrazov je ena izmed najbolj uspešnih aplikacij interaktivnega evolucijskega
ra?cunanja. Kljub temu pa do zdaj na podro?cju kompozitne gradnje niso bile podrobno raziskane
možnosti snovanja algoritma. To vprašanje smo obravnavali s štirimi poskusi. V prvem je uporabljeno
sortiranje za identifikacijo 12 najbolj izstopajo?cih dimenzij 30-dimenzionalnega preiskovalnega prostora.
V drugem primerjamo u?cinkovitost dveh mutacij in dveh rekombinacijskih operaterjev za interaktivni
genetski algoritem. V tretjem primerjamo tri preiskovalne prostore: 30-dimenzionalni, matemati?cno reducirani
12-dimenzionalni in 12-dimenzionalni prostor sestavljen iz 12 najpomembnejših dimenzij. Na
koncu smo primerjali uspešnost interaktivnega genetskega algoritma z interaktivno diferencialno evolucijo.
Rezultati kažejo, da je proces kompozitne gradnje obrazov izredno robusten glede na izbiro evolucijskega
operatorja(-ev), dimenzionalnost preiskovalnega prostora in izbiro interaktivnega evolucijskega algoritma.
To pripisujemo nenatan?cni naravi percepcije in razlikam med interakcijami uporabnikov z algoritmom
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Can graph-cutting improve microarray gene expression reconstructions?
Microarrays produce high-resolution image data that are, unfortunately, permeated with a great deal of “noise” that must be removed for precision purposes. This paper presents a technique for such a removal process. On completion of this non-trivial task, a new surface (devoid of gene spots) is subtracted from the original to render more precise gene expressions. The graph-cutting technique as implemented has the benefits that only the most appropriate pixels are replaced and these replacements are replicates rather than estimates. This means the influence of outliers and other artifacts are handled more appropriately (than in previous methods) as well as the variability of the final gene expressions being considerably reduced. Experiments are carried out to test the technique against commercial and previously researched reconstruction methods. Final results show that the graph-cutting inspired identification mechanism has a positive significant impact on reconstruction accuracy
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