48 research outputs found
PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features
Designing an end-to-end deep learning network to match the biometric features
with limited training samples is an extremely challenging task. To address this
problem, we propose a new way to design an end-to-end deep CNN framework i.e.,
PVSNet that works in two major steps: first, an encoder-decoder network is used
to learn generative domain-specific features followed by a Siamese network in
which convolutional layers are pre-trained in an unsupervised fashion as an
autoencoder. The proposed model is trained via triplet loss function that is
adjusted for learning feature embeddings in a way that minimizes the distance
between embedding-pairs from the same subject and maximizes the distance with
those from different subjects, with a margin. In particular, a triplet Siamese
matching network using an adaptive margin based hard negative mining has been
suggested. The hyper-parameters associated with the training strategy, like the
adaptive margin, have been tuned to make the learning more effective on
biometric datasets. In extensive experimentation, the proposed network
outperforms most of the existing deep learning solutions on three type of
typical vein datasets which clearly demonstrates the effectiveness of our
proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security
and Behavior Analysis (ISBA), 2019, Hyderabad, Indi
UBSegNet: Unified Biometric Region of Interest Segmentation Network
Digital human identity management, can now be seen as a social necessity, as
it is essentially required in almost every public sector such as, financial
inclusions, security, banking, social networking e.t.c. Hence, in today's
rampantly emerging world with so many adversarial entities, relying on a single
biometric trait is being too optimistic. In this paper, we have proposed a
novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for
extracting region of interest from five different biometric traits viz. face,
iris, palm, knuckle and 4-slap fingerprint. The architecture of the proposed
UBSegNet consists of two stages: (i) Trait classification and (ii) Trait
localization. For these stages, we have used a state of the art region based
convolutional neural network (RCNN), comprising of three major parts namely
convolutional layers, region proposal network (RPN) along with classification
and regression heads. The model has been evaluated over various huge publicly
available biometric databases. To the best of our knowledge this is the first
unified architecture proposed, segmenting multiple biometric traits. It has
been tested over around 5000 * 5 = 25,000 images (5000 images per trait) and
produces very good results. Our work on unified biometric segmentation, opens
up the vast opportunities in the field of multiple biometric traits based
authentication systems.Comment: 4th Asian Conference on Pattern Recognition (ACPR 2017