761 research outputs found
Comparator Networks
The objective of this work is set-based verification, e.g. to decide if two
sets of images of a face are of the same person or not. The traditional
approach to this problem is to learn to generate a feature vector per image,
aggregate them into one vector to represent the set, and then compute the
cosine similarity between sets. Instead, we design a neural network
architecture that can directly learn set-wise verification. Our contributions
are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of
sets (each may contain a variable number of images) as inputs, and compute a
similarity between the pair--this involves attending to multiple discriminative
local regions (landmarks), and comparing local descriptors between pairs of
faces; (ii) To encourage high-quality representations for each set, internal
competition is introduced for recalibration based on the landmark score; (iii)
Inspired by image retrieval, a novel hard sample mining regime is proposed to
control the sampling process, such that the DCN is complementary to the
standard image classification models. Evaluations on the IARPA Janus face
recognition benchmarks show that the comparator networks outperform the
previous state-of-the-art results by a large margin.Comment: To appear in ECCV 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
An Efficient Method for Number Plate Detection and Extraction Using White Pixel Detection (WPD) Method
Intelligent transport systems play an important role in supporting smart cities because of their promising applications in various areas, such as electronic toll collection, highway surveillance, urban logistics and traffic management. One of the key components of intelligent transport systems is vehicle license plate recognition, which enables the identification of each vehicle by recognizing the characters on its license plate through various image processing and computer vision techniques. Vehicle license plate recognition typically consists of smoothing image using median filter, White pixel detection (WPD), and number plate extraction. In this work an efficient White pixel detection method has been describing a license plates in various luminance conditions. Mostly we will focus on vehicle number plate detection along with the white pixel detection method we will use median filters and Line density filters to increase the detection accuracy for number plate. Subjective and objective quality assessment parameters will give us robustness of proposed work compared to state of License Plate Detection(LPD) techniques
UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition
Recently proposed robust 3D face alignment methods establish either dense or
sparse correspondence between a 3D face model and a 2D facial image. The use of
these methods presents new challenges as well as opportunities for facial
texture analysis. In particular, by sampling the image using the fitted model,
a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map
is always incomplete. In this paper, we propose a framework for training Deep
Convolutional Neural Network (DCNN) to complete the facial UV map extracted
from in-the-wild images. To this end, we first gather complete UV maps by
fitting a 3D Morphable Model (3DMM) to various multiview image and video
datasets, as well as leveraging on a new 3D dataset with over 3,000 identities.
Second, we devise a meticulously designed architecture that combines local and
global adversarial DCNNs to learn an identity-preserving facial UV completion
model. We demonstrate that by attaching the completed UV to the fitted mesh and
generating instances of arbitrary poses, we can increase pose variations for
training deep face recognition/verification models, and minimise pose
discrepancy during testing, which lead to better performance. Experiments on
both controlled and in-the-wild UV datasets prove the effectiveness of our
adversarial UV completion model. We achieve state-of-the-art verification
accuracy, , under the CFP frontal-profile protocol only by combining
pose augmentation during training and pose discrepancy reduction during
testing. We will release the first in-the-wild UV dataset (we refer as WildUV)
that comprises of complete facial UV maps from 1,892 identities for research
purposes
- …