2 research outputs found
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Heterogeneous face recognition (HFR) refers to matching face images acquired
from different domains with wide applications in security scenarios. This paper
presents a deep neural network approach namely Multi-Margin based Decorrelation
Learning (MMDL) to extract decorrelation representations in a hyperspherical
space for cross-domain face images. The proposed framework can be divided into
two components: heterogeneous representation network and decorrelation
representation learning. First, we employ a large scale of accessible visual
face images to train heterogeneous representation network. The decorrelation
layer projects the output of the first component into decorrelation latent
subspace and obtains decorrelation representation. In addition, we design a
multi-margin loss (MML), which consists of quadruplet margin loss (QML) and
heterogeneous angular margin loss (HAML), to constrain the proposed framework.
Experimental results on two challenging heterogeneous face databases show that
our approach achieves superior performance on both verification and recognition
tasks, comparing with state-of-the-art methods.Comment: IJCAI 201
Local Multi-Grouped Binary Descriptor with Ring-based Pooling Configuration and Optimization
Local binary descriptors are attracting increasingly attention due to their
great advantages in computational speed, which are able to achieve real-time
performance in numerous image/vision applications. Various methods have been
proposed to learn data-dependent binary descriptors. However, most existing
binary descriptors aim overly at computational simplicity at the expense of
significant information loss which causes ambiguity in similarity measure using
Hamming distance. In this paper, by considering multiple features might share
complementary information, we present a novel local binary descriptor, referred
as Ring-based Multi-Grouped Descriptor (RMGD), to successfully bridge the
performance gap between current binary and floated-point descriptors. Our
contributions are two-fold. Firstly, we introduce a new pooling configuration
based on spatial ring-region sampling, allowing for involving binary tests on
the full set of pairwise regions with different shapes, scales and distances.
This leads to a more meaningful description than existing methods which
normally apply a limited set of pooling configurations. Then, an extended
Adaboost is proposed for efficient bit selection by emphasizing high variance
and low correlation, achieving a highly compact representation. Secondly, the
RMGD is computed from multiple image properties where binary strings are
extracted. We cast multi-grouped features integration as rankSVM or sparse SVM
learning problem, so that different features can compensate strongly for each
other, which is the key to discriminativeness and robustness. The performance
of RMGD was evaluated on a number of publicly available benchmarks, where the
RMGD outperforms the state-of-the-art binary descriptors significantly.Comment: To appear in IEEE Trans. on Image Processing, 201