4,796 research outputs found
Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach
Different from face verification, face identification is much more demanding.
To reach comparable performance, an identifier needs to be roughly N times
better than a verifier. To expect a breakthrough in face identification, we
need a fresh look at the fundamental building blocks of face recognition. In
this paper we focus on the selection of a suitable signal representation and
better matching strategy for face identification. We demonstrate how Gabor
phase could be leveraged to improve the performance of face identification by
using the Block Matching method. Compared to the existing approaches, the
proposed method features much lower algorithmic complexity: face images are
only filtered by a single-scale Gabor filter pair and the matching is performed
between any pairs of face images at hand without involving any training
process. Benchmark evaluations show that the proposed approach is totally
comparable to and even better than state-of-the-art algorithms, which are
typically based on more features extracted from a large set of Gabor faces
and/or rely on heavy training processes
Fast Matching by 2 Lines of Code for Large Scale Face Recognition Systems
In this paper, we propose a method to apply the popular cascade classifier
into face recognition to improve the computational efficiency while keeping
high recognition rate. In large scale face recognition systems, because the
probability of feature templates coming from different subjects is very high,
most of the matching pairs will be rejected by the early stages of the cascade.
Therefore, the cascade can improve the matching speed significantly. On the
other hand, using the nested structure of the cascade, we could drop some
stages at the end of feature to reduce the memory and bandwidth usage in some
resources intensive system while not sacrificing the performance too much. The
cascade is learned by two steps. Firstly, some kind of prepared features are
grouped into several nested stages. And then, the threshold of each stage is
learned to achieve user defined verification rate (VR). In the paper, we take a
landmark based Gabor+LDA face recognition system as baseline to illustrate the
process and advantages of the proposed method. However, the use of this method
is very generic and not limited in face recognition, which can be easily
generalized to other biometrics as a post-processing module. Experiments on the
FERET database show the good performance of our baseline and an experiment on a
self-collected large scale database illustrates that the cascade can improve
the matching speed significantly
Face Recognition: From Traditional to Deep Learning Methods
Starting in the seventies, face recognition has become one of the most
researched topics in computer vision and biometrics. Traditional methods based
on hand-crafted features and traditional machine learning techniques have
recently been superseded by deep neural networks trained with very large
datasets. In this paper we provide a comprehensive and up-to-date literature
review of popular face recognition methods including both traditional
(geometry-based, holistic, feature-based and hybrid methods) and deep learning
methods
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification
In this paper, we propose a novel person Re-ID model, Consecutive Batch
DropBlock Network (CBDB-Net), to capture the attentive and robust person
descriptor for the person Re-ID task. The CBDB-Net contains two novel designs:
the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL). In
the Consecutive Batch DropBlock Module (CBDBM), we firstly conduct uniform
partition on the feature maps. And then, we independently and continuously drop
each patch from top to bottom on the feature maps, which can output multiple
incomplete feature maps. In the training stage, these multiple incomplete
features can better encourage the Re-ID model to capture the robust person
descriptor for the Re-ID task. In the Elastic Loss (EL), we design a novel
weight control item to help the Re-ID model adaptively balance hard sample
pairs and easy sample pairs in the whole training process. Through an extensive
set of ablation studies, we verify that the Consecutive Batch DropBlock Module
(CBDBM) and the Elastic Loss (EL) each contribute to the performance boosts of
CBDB-Net. We demonstrate that our CBDB-Net can achieve the competitive
performance on the three standard person Re-ID datasets (the Market-1501, the
DukeMTMC-Re-ID, and the CUHK03 dataset), three occluded Person Re-ID datasets
(the Occluded DukeMTMC, the Partial-REID, and the Partial iLIDS dataset), and a
general image retrieval dataset (In-Shop Clothes Retrieval dataset).Comment: Acceped by IEEE Transactions on Circuits and Systems for Video
Technology (TCSVT
4D Human Body Correspondences from Panoramic Depth Maps
The availability of affordable 3D full body reconstruction systems has given
rise to free-viewpoint video (FVV) of human shapes. Most existing solutions
produce temporally uncorrelated point clouds or meshes with unknown
point/vertex correspondences. Individually compressing each frame is
ineffective and still yields to ultra-large data sizes. We present an
end-to-end deep learning scheme to establish dense shape correspondences and
subsequently compress the data. Our approach uses sparse set of "panoramic"
depth maps or PDMs, each emulating an inward-viewing concentric mosaics. We
then develop a learning-based technique to learn pixel-wise feature descriptors
on PDMs. The results are fed into an autoencoder-based network for compression.
Comprehensive experiments demonstrate our solution is robust and effective on
both public and our newly captured datasets.Comment: 10 pages, 12 figures, CVPR 2018 pape
Perceptually Motivated Shape Context Which Uses Shape Interiors
In this paper, we identify some of the limitations of current-day shape
matching techniques. We provide examples of how contour-based shape matching
techniques cannot provide a good match for certain visually similar shapes. To
overcome this limitation, we propose a perceptually motivated variant of the
well-known shape context descriptor. We identify that the interior properties
of the shape play an important role in object recognition and develop a
descriptor that captures these interior properties. We show that our method can
easily be augmented with any other shape matching algorithm. We also show from
our experiments that the use of our descriptor can significantly improve the
retrieval rates
ENIGMA: Evolutionary Non-Isometric Geometry Matching
In this paper we propose a fully automatic method for shape correspondence
that is widely applicable, and especially effective for non isometric shapes
and shapes of different topology. We observe that fully-automatic shape
correspondence can be decomposed as a hybrid discrete/continuous optimization
problem, and we find the best sparse landmark correspondence, whose
sparse-to-dense extension minimizes a local metric distortion. To tackle the
combinatorial task of landmark correspondence we use an evolutionary genetic
algorithm, where the local distortion of the sparse-to-dense extension is used
as the objective function. We design novel geometrically guided genetic
operators, which, when combined with our objective, are highly effective for
non isometric shape matching. Our method outperforms state of the art methods
for automatic shape correspondence both quantitatively and qualitatively on
challenging datasets
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