33 research outputs found
Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation
Heterogeneous Face Recognition (HFR) aims to match face images across
different domains, such as thermal and visible spectra, expanding the
applicability of Face Recognition (FR) systems to challenging scenarios.
However, the domain gap and limited availability of large-scale datasets in the
target domain make training robust and invariant HFR models from scratch
difficult. In this work, we treat different modalities as distinct styles and
propose a framework to adapt feature maps, bridging the domain gap. We
introduce a novel Conditional Adaptive Instance Modulation (CAIM) module that
can be integrated into pre-trained FR networks, transforming them into HFR
networks. The CAIM block modulates intermediate feature maps, to adapt the
style of the target modality effectively bridging the domain gap. Our proposed
method allows for end-to-end training with a minimal number of paired samples.
We extensively evaluate our approach on multiple challenging benchmarks,
demonstrating superior performance compared to state-of-the-art methods. The
source code and protocols for reproducing the findings will be made publicly
available.Comment: Accepted for publication in IJCB 202
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
Face recognition has evolved as a widely used biometric modality. However,
its vulnerability against presentation attacks poses a significant security
threat. Though presentation attack detection (PAD) methods try to address this
issue, they often fail in generalizing to unseen attacks. In this work, we
propose a new framework for PAD using a one-class classifier, where the
representation used is learned with a Multi-Channel Convolutional Neural
Network (MCCNN). A novel loss function is introduced, which forces the network
to learn a compact embedding for bonafide class while being far from the
representation of attacks. A one-class Gaussian Mixture Model is used on top of
these embeddings for the PAD task. The proposed framework introduces a novel
approach to learn a robust PAD system from bonafide and available (known)
attack classes. This is particularly important as collecting bonafide data and
simpler attacks are much easier than collecting a wide variety of expensive
attacks. The proposed system is evaluated on the publicly available WMCA
multi-channel face PAD database, which contains a wide variety of 2D and 3D
attacks. Further, we have performed experiments with MLFP and SiW-M datasets
using RGB channels only. Superior performance in unseen attack protocols shows
the effectiveness of the proposed approach. Software, data, and protocols to
reproduce the results are made available publicly.Comment: 15 page
An Improved Algorithm for Eye Corner Detection
In this paper, a modified algorithm for the detection of nasal and temporal
eye corners is presented. The algorithm is a modification of the Santos and
Proenka Method. In the first step, we detect the face and the eyes using
classifiers based on Haar-like features. We then segment out the sclera, from
the detected eye region. From the segmented sclera, we segment out an
approximate eyelid contour. Eye corner candidates are obtained using Harris and
Stephens corner detector. We introduce a post-pruning of the Eye corner
candidates to locate the eye corners, finally. The algorithm has been tested on
Yale, JAFFE databases as well as our created database
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
The vulnerability of face recognition systems to presentation attacks has
limited their application in security-critical scenarios. Automatic methods of
detecting such malicious attempts are essential for the safe use of facial
recognition technology. Although various methods have been suggested for
detecting such attacks, most of them over-fit the training set and fail in
generalizing to unseen attacks and environments. In this work, we use transfer
learning from the vision transformer model for the zero-shot anti-spoofing
task. The effectiveness of the proposed approach is demonstrated through
experiments in publicly available datasets. The proposed approach outperforms
the state-of-the-art methods in the zero-shot protocols in the HQ-WMCA and
SiW-M datasets by a large margin. Besides, the model achieves a significant
boost in cross-database performance as well.Comment: 8 pages, 3 figures, Accepted for Publication in IJCB202