18,365 research outputs found
Deep Learning Face Representation by Joint Identification-Verification
The key challenge of face recognition is to develop effective feature
representations for reducing intra-personal variations while enlarging
inter-personal differences. In this paper, we show that it can be well solved
with deep learning and using both face identification and verification signals
as supervision. The Deep IDentification-verification features (DeepID2) are
learned with carefully designed deep convolutional networks. The face
identification task increases the inter-personal variations by drawing DeepID2
extracted from different identities apart, while the face verification task
reduces the intra-personal variations by pulling DeepID2 extracted from the
same identity together, both of which are essential to face recognition. The
learned DeepID2 features can be well generalized to new identities unseen in
the training data. On the challenging LFW dataset, 99.15% face verification
accuracy is achieved. Compared with the best deep learning result on LFW, the
error rate has been significantly reduced by 67%
Privacy-Preserving Facial Recognition Using Biometric-Capsules
Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design.
In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods
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
- …