11,032 research outputs found
Automatic Face Recognition System Based on Local Fourier-Bessel Features
We present an automatic face verification system inspired by known properties
of biological systems. In the proposed algorithm the whole image is converted
from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT).
Using the whole image is compared to the case where only face image regions
(local analysis) are considered. The resulting representations are embedded in
a dissimilarity space, where each image is represented by its distance to all
the other images, and a Pseudo-Fisher discriminator is built. Verification test
results on the FERET database showed that the local-based algorithm outperforms
the global-FBT version. The local-FBT algorithm performed as state-of-the-art
methods under different testing conditions, indicating that the proposed system
is highly robust for expression, age, and illumination variations. We also
evaluated the performance of the proposed system under strong occlusion
conditions and found that it is highly robust for up to 50% of face occlusion.
Finally, we automated completely the verification system by implementing face
and eye detection algorithms. Under this condition, the local approach was only
slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing,
18 (SIBGRAPI
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
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