2 research outputs found
Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
Mobile biometric approaches provide the convenience of secure authentication
with an omnipresent technology. However, this brings an additional challenge of
recognizing biometric patterns in unconstrained environment including
variations in mobile camera sensors, illumination conditions, and capture
distance. To address the heterogeneous challenge, this research presents a
novel heterogeneity aware loss function within a deep learning framework. The
effectiveness of the proposed loss function is evaluated for periocular
biometrics using the CSIP, IMP and VISOB mobile periocular databases. The
results show that the proposed algorithm yields state-of-the-art results in a
heterogeneous environment and improves generalizability for cross-database
experiments
Anomaly Detection-Based Unknown Face Presentation Attack Detection
Anomaly detection-based spoof attack detection is a recent development in
face Presentation Attack Detection (fPAD), where a spoof detector is learned
using only non-attacked images of users. These detectors are of practical
importance as they are shown to generalize well to new attack types. In this
paper, we present a deep-learning solution for anomaly detection-based spoof
attack detection where both classifier and feature representations are learned
together end-to-end. First, we introduce a pseudo-negative class during
training in the absence of attacked images. The pseudo-negative class is
modeled using a Gaussian distribution whose mean is calculated by a weighted
running mean. Secondly, we use pairwise confusion loss to further regularize
the training process. The proposed approach benefits from the representation
learning power of the CNNs and learns better features for fPAD task as shown in
our ablation study. We perform extensive experiments on four publicly available
datasets: Replay-Attack, Rose-Youtu, OULU-NPU and Spoof in Wild to show the
effectiveness of the proposed approach over the previous methods. Code is
available at: \url{https://github.com/yashasvi97/IJCB2020_anomaly