98 research outputs found
Age-Adaptive Multimodal Biometric Authentication System with Blockchain-based Re-Enrollment
In the long run, a significant time gap between enrollment and probe image challenges the model's prediction ability when it has been trained on variant biometric traits. Since variant biometric traits change over time, it is sensible to construct a multimodal biometric authentication system that must include at least one invariant trait, such as the iris. The emergence of Deep learning has enabled developers to build classifiers on synthesized age-progressive images, particularly face images, to search for individuals who have been missing for many years, to avail a comprehensive portrayal of their appearance. However, in sensitive areas such as the military and banks, where security and confidentiality are of utmost importance, models should be built using real samples, and any variations in biometric traits should trigger an alert for the system and notify the subject about re-enrollment. This paper proposes an algorithm for age adaptation of biometric classifiers using multimodal channels which securely update the biometric traits while logging the transactions on the blockchain. It emphasizes confidence-score-based re-enrolment of individual subjects when the authenticator module becomes less effective with a particular subject's probe image. This reduces the time, cost, and memory involved in periodic re-enrolment of all subjects. The classifier deployed on the blockchain invokes appropriate smart contracts and completes this process securely
Tackling Age-Invariant Face Recognition with Non-Linear PLDA and Pairwise SVM
Face recognition approaches, especially those based on deep learning models, are becoming increasingly attractive for missing person identification, due to their effectiveness and the relative simplicity of obtaining information available for comparison. However, these methods still suffer from large accuracy drops when they have to tackle cross-age recognition, which is the most common condition to face in this specific task.
To address these challenges, in this paper we investigate the contribution of different generative and discriminative models that extend the Probabilistic Linear Discriminant Analysis (PLDA) approach. These models aim at disentangling identity from other facial variations (including those due to age effects). As such, they can improve the age invariance characteristics of state-of-the-art deep facial embeddings. In this work, we experiment with a standard PLDA, a non-linear version of PLDA, the Pairwise Support Vector Machine (PSVM), and introduce a nonlinear version of PSVM (NL--PSVM) as a novelty. We thoroughly analyze the proposed models' performance when addressing cross-age recognition in a large and challenging experimental dataset containing around 2.5 million images of 790,000 individuals. Results on this testbed confirm the challenges in age invariant face recognition, showing significant differences in the effects of aging across embedding models, genders, age ranges, and age gaps. Our experiments show as well the effectiveness of both PLDA and its proposed extensions in reducing the age sensitivity of the facial features, especially when there are significant age differences (more than ten years) between the compared images or when age-related facial changes are more pronounced, such as during the transition from childhood to adolescence or from adolescence to adulthood. Further experiments on three standard cross-age benchmarks (MORPH2, CACD-VS and FG-NET) confirm the proposed models' effectiveness
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
To minimize the impact of age variation on face recognition, age-invariant
face recognition (AIFR) extracts identity-related discriminative features by
minimizing the correlation between identity- and age-related features while
face age synthesis (FAS) eliminates age variation by converting the faces in
different age groups to the same group. However, AIFR lacks visual results for
model interpretation and FAS compromises downstream recognition due to
artifacts. Therefore, we propose a unified, multi-task framework to jointly
handle these two tasks, termed MTLFace, which can learn the age-invariant
identity-related representation for face recognition while achieving pleasing
face synthesis for model interpretation. Specifically, we propose an
attention-based feature decomposition to decompose the mixed face features into
two uncorrelated components -- identity- and age-related features -- in a
spatially constrained way. Unlike the conventional one-hot encoding that
achieves group-level FAS, we propose a novel identity conditional module to
achieve identity-level FAS, which can improve the age smoothness of synthesized
faces through a weight-sharing strategy. Benefiting from the proposed
multi-task framework, we then leverage those high-quality synthesized faces
from FAS to further boost AIFR via a novel selective fine-tuning strategy.
Furthermore, to advance both AIFR and FAS, we collect and release a large
cross-age face dataset with age and gender annotations, and a new benchmark
specifically designed for tracing long-missing children. Extensive experimental
results on five benchmark cross-age datasets demonstrate that MTLFace yields
superior performance for both AIFR and FAS. We further validate MTLFace on two
popular general face recognition datasets, obtaining competitive performance on
face recognition in the wild. Code is available at
http://hzzone.github.io/MTLFace.Comment: TPAMI 2022. arXiv admin note: substantial text overlap with
arXiv:2103.0152
NoPeek: Information leakage reduction to share activations in distributed deep learning
For distributed machine learning with sensitive data, we demonstrate how
minimizing distance correlation between raw data and intermediary
representations reduces leakage of sensitive raw data patterns across client
communications while maintaining model accuracy. Leakage (measured using
distance correlation between input and intermediate representations) is the
risk associated with the invertibility of raw data from intermediary
representations. This can prevent client entities that hold sensitive data from
using distributed deep learning services. We demonstrate that our method is
resilient to such reconstruction attacks and is based on reduction of distance
correlation between raw data and learned representations during training and
inference with image datasets. We prevent such reconstruction of raw data while
maintaining information required to sustain good classification accuracies
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
- …