51 research outputs found
Synthetic Data for Face Recognition: Current State and Future Prospects
Over the past years, deep learning capabilities and the availability of
large-scale training datasets advanced rapidly, leading to breakthroughs in
face recognition accuracy. However, these technologies are foreseen to face a
major challenge in the next years due to the legal and ethical concerns about
using authentic biometric data in AI model training and evaluation along with
increasingly utilizing data-hungry state-of-the-art deep learning models. With
the recent advances in deep generative models and their success in generating
realistic and high-resolution synthetic image data, privacy-friendly synthetic
data has been recently proposed as an alternative to privacy-sensitive
authentic data to overcome the challenges of using authentic data in face
recognition development. This work aims at providing a clear and structured
picture of the use-cases taxonomy of synthetic face data in face recognition
along with the recent emerging advances of face recognition models developed on
the bases of synthetic data. We also discuss the challenges facing the use of
synthetic data in face recognition development and several future prospects of
synthetic data in the domain of face recognition.Comment: Accepted at Image and Vision Computing 2023 (IVC 2023
MENTOR: Human Perception-Guided Pretraining for Iris Presentation Detection
Incorporating human salience into the training of CNNs has boosted
performance in difficult tasks such as biometric presentation attack detection.
However, collecting human annotations is a laborious task, not to mention the
questions of how and where (in the model architecture) to efficiently
incorporate this information into model's training once annotations are
obtained. In this paper, we introduce MENTOR (huMan pErceptioN-guided
preTraining fOr iris pResentation attack detection), which addresses both of
these issues through two unique rounds of training. First, we train an
autoencoder to learn human saliency maps given an input iris image (both real
and fake examples). Once this representation is learned, we utilize the trained
autoencoder in two different ways: (a) as a pre-trained backbone for an iris
presentation attack detector, and (b) as a human-inspired annotator of salient
features on unknown data. We show that MENTOR's benefits are threefold: (a)
significant boost in iris PAD performance when using the human
perception-trained encoder's weights compared to general-purpose weights (e.g.
ImageNet-sourced, or random), (b) capability of generating infinite number of
human-like saliency maps for unseen iris PAD samples to be used in any human
saliency-guided training paradigm, and (c) increase in efficiency of iris PAD
model training. Sources codes and weights are offered along with the paper.Comment: 8 pages, 3 figure
A Comprehensive Study on Face Recognition Biases Beyond Demographics
Face recognition (FR) systems have a growing effect on critical
decision-making processes. Recent works have shown that FR solutions show
strong performance differences based on the user's demographics. However, to
enable a trustworthy FR technology, it is essential to know the influence of an
extended range of facial attributes on FR beyond demographics. Therefore, in
this work, we analyse FR bias over a wide range of attributes. We investigate
the influence of 47 attributes on the verification performance of two popular
FR models. The experiments were performed on the publicly available MAADFace
attribute database with over 120M high-quality attribute annotations. To
prevent misleading statements about biased performances, we introduced control
group based validity values to decide if unbalanced test data causes the
performance differences. The results demonstrate that also many non-demographic
attributes strongly affect the recognition performance, such as accessories,
hair-styles and colors, face shapes, or facial anomalies. The observations of
this work show the strong need for further advances in making FR system more
robust, explainable, and fair. Moreover, our findings might help to a better
understanding of how FR networks work, to enhance the robustness of these
networks, and to develop more generalized bias-mitigating face recognition
solutions.Comment: Under review in IEEE Transactions on Technology and Societ
Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features.
Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation
Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models
This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, knowledge distillation (kd) was previously proposed to carry this knowledge from a large model (teacher) into a small model (student). Conventional KD optimizes the student output to be similar to the teacher output (commonly classification output). In biometrics, comparison (verification) and storage operations are conducted on biometric templates, extracted from pre-classification layers. In this work, we propose a novel template-driven KD approach that optimizes the distillation process so that the student model learns to produce templates similar to those produced by the teacher model. We demonstrate our approach on intra- and cross-device periocular verification. Our results demonstrate the superiority of our proposed approach over a network trained without KD and networks trained with conventional (vanilla) KD. For example, the targeted small model achieved an equal error rate (EER) value of 22.2% on cross-device verification without KD. The same model achieved an EER of 21.9% with the conventional KD, and only 14.7% EER when using our proposed template-driven KD
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