1,115 research outputs found
Coupled Deep Learning for Heterogeneous Face Recognition
Heterogeneous face matching is a challenge issue in face recognition due to
large domain difference as well as insufficient pairwise images in different
modalities during training. This paper proposes a coupled deep learning (CDL)
approach for the heterogeneous face matching. CDL seeks a shared feature space
in which the heterogeneous face matching problem can be approximately treated
as a homogeneous face matching problem. The objective function of CDL mainly
includes two parts. The first part contains a trace norm and a block-diagonal
prior as relevance constraints, which not only make unpaired images from
multiple modalities be clustered and correlated, but also regularize the
parameters to alleviate overfitting. An approximate variational formulation is
introduced to deal with the difficulties of optimizing low-rank constraint
directly. The second part contains a cross modal ranking among triplet domain
specific images to maximize the margin for different identities and increase
data for a small amount of training samples. Besides, an alternating
minimization method is employed to iteratively update the parameters of CDL.
Experimental results show that CDL achieves better performance on the
challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch
database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF),
which significantly outperforms state-of-the-art heterogeneous face recognition
methods.Comment: AAAI 201
Rethinking the Domain Gap in Near-infrared Face Recognition
Heterogeneous face recognition (HFR) involves the intricate task of matching
face images across the visual domains of visible (VIS) and near-infrared (NIR).
While much of the existing literature on HFR identifies the domain gap as a
primary challenge and directs efforts towards bridging it at either the input
or feature level, our work deviates from this trend. We observe that large
neural networks, unlike their smaller counterparts, when pre-trained on large
scale homogeneous VIS data, demonstrate exceptional zero-shot performance in
HFR, suggesting that the domain gap might be less pronounced than previously
believed. By approaching the HFR problem as one of low-data fine-tuning, we
introduce a straightforward framework: comprehensive pre-training, succeeded by
a regularized fine-tuning strategy, that matches or surpasses the current
state-of-the-art on four publicly available benchmarks. Corresponding codes can
be found at https://github.com/michaeltrs/RethinkNIRVIS.Comment: 5 pages, 3 figures, 6 table
- …