3,043 research outputs found
Scale Selective Extended Local Binary Pattern for Texture Classification
In this paper, we propose a new texture descriptor, scale selective extended
local binary pattern (SSELBP), to characterize texture images with scale
variations. We first utilize multi-scale extended local binary patterns (ELBP)
with rotation-invariant and uniform mappings to capture robust local micro- and
macro-features. Then, we build a scale space using Gaussian filters and
calculate the histogram of multi-scale ELBPs for the image at each scale.
Finally, we select the maximum values from the corresponding bins of
multi-scale ELBP histograms at different scales as scale-invariant features. A
comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows
that the proposed SSELBP has high accuracy comparable to state-of-the-art
texture descriptors on gray-scale-, rotation-, and scale-invariant texture
classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 201
A multimodal deep learning framework using local feature representations for face recognition
YesThe most recent face recognition systems are
mainly dependent on feature representations obtained using
either local handcrafted-descriptors, such as local binary patterns
(LBP), or use a deep learning approach, such as deep
belief network (DBN). However, the former usually suffers
from the wide variations in face images, while the latter
usually discards the local facial features, which are proven
to be important for face recognition. In this paper, a novel
framework based on merging the advantages of the local
handcrafted feature descriptors with the DBN is proposed to
address the face recognition problem in unconstrained conditions.
Firstly, a novel multimodal local feature extraction
approach based on merging the advantages of the Curvelet
transform with Fractal dimension is proposed and termed
the Curvelet–Fractal approach. The main motivation of this
approach is that theCurvelet transform, a newanisotropic and
multidirectional transform, can efficiently represent themain
structure of the face (e.g., edges and curves), while the Fractal
dimension is one of the most powerful texture descriptors
for face images. Secondly, a novel framework is proposed,
termed the multimodal deep face recognition (MDFR)framework,
to add feature representations by training aDBNon top
of the local feature representations instead of the pixel intensity
representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary
to those acquired by the Curvelet–Fractal approach.
Finally, the performance of the proposed approaches has
been evaluated by conducting a number of extensive experiments
on four large-scale face datasets: the SDUMLA-HMT,
FERET, CAS-PEAL-R1, and LFW databases. The results
obtained from the proposed approaches outperform other
state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by
achieving new state-of-the-art results on all the employed
datasets
PCA-ANN Face Recognition System based on Photometric Normalization Techniques
The human face is the main focus of attention in social interaction, and is also the major key in conveying identity and emotion of a person. It has the appealing characteristic of not being intrusive as compared with other biometric techniques. The research works on face recognition started in the 1960s with the pioneering work of Bledsoe and Kanade, wh
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