140 research outputs found
One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations
One weakness of machine-learning algorithms is the need to train the models
for a new task. This presents a specific challenge for biometric recognition
due to the dynamic nature of databases and, in some instances, the reliance on
subject collaboration for data collection. In this paper, we investigate the
behavior of deep representations in widely used CNN models under extreme data
scarcity for One-Shot periocular recognition, a biometric recognition task. We
analyze the outputs of CNN layers as identity-representing feature vectors. We
examine the impact of Domain Adaptation on the network layers' output for
unseen data and evaluate the method's robustness concerning data normalization
and generalization of the best-performing layer. We improved state-of-the-art
results that made use of networks trained with biometric datasets with millions
of images and fine-tuned for the target periocular dataset by utilizing
out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard
computer vision algorithms. For example, for the Cross-Eyed dataset, we could
reduce the EER by 67% and 79% (from 1.70% and 3.41% to 0.56% and 0.71%) in the
Close-World and Open-World protocols, respectively, for the periocular case. We
also demonstrate that traditional algorithms like SIFT can outperform CNNs in
situations with limited data or scenarios where the network has not been
trained with the test classes like the Open-World mode. SIFT alone was able to
reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for
Cross-Eyed in the Close-World and Open-World protocols, respectively, and a
reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the
Open-World and single biometric case.Comment: Submitted preprint to IEE Acces
Cross-Spectral Periocular Recognition with Conditional Adversarial Networks
This work addresses the challenge of comparing periocular images captured in
different spectra, which is known to produce significant drops in performance
in comparison to operating in the same spectrum. We propose the use of
Conditional Generative Adversarial Networks, trained to con-vert periocular
images between visible and near-infrared spectra, so that biometric
verification is carried out in the same spectrum. The proposed setup allows the
use of existing feature methods typically optimized to operate in a single
spectrum. Recognition experiments are done using a number of off-the-shelf
periocular comparators based both on hand-crafted features and CNN descriptors.
Using the Hong Kong Polytechnic University Cross-Spectral Iris Images Database
(PolyU) as benchmark dataset, our experiments show that cross-spectral
performance is substantially improved if both images are converted to the same
spectrum, in comparison to matching features extracted from images in different
spectra. In addition to this, we fine-tune a CNN based on the ResNet50
architecture, obtaining a cross-spectral periocular performance of EER=1%, and
GAR>99% @ FAR=1%, which is comparable to the state-of-the-art with the PolyU
database.Comment: Accepted for publication at 2020 International Joint Conference on
Biometrics (IJCB 2020
Log-Likelihood Score Level Fusion for Improved Cross-Sensor Smartphone Periocular Recognition
The proliferation of cameras and personal devices results in a wide
variability of imaging conditions, producing large intra-class variations and a
significant performance drop when images from heterogeneous environments are
compared. However, many applications require to deal with data from different
sources regularly, thus needing to overcome these interoperability problems.
Here, we employ fusion of several comparators to improve periocular performance
when images from different smartphones are compared. We use a probabilistic
fusion framework based on linear logistic regression, in which fused scores
tend to be log-likelihood ratios, obtaining a reduction in cross-sensor EER of
up to 40% due to the fusion. Our framework also provides an elegant and simple
solution to handle signals from different devices, since same-sensor and
cross-sensor score distributions are aligned and mapped to a common
probabilistic domain. This allows the use of Bayes thresholds for optimal
decision-making, eliminating the need of sensor-specific thresholds, which is
essential in operational conditions because the threshold setting critically
determines the accuracy of the authentication process in many applications.Comment: Published at Proc. 25th European Signal Processing Conference,
EUSIPCO 2017. arXiv admin note: text overlap with arXiv:1902.0812
On the Effect of Selfie Beautification Filters on Face Detection and Recognition
Beautification and augmented reality filters are very popular in applications
that use selfie images captured with smartphones or personal devices. However,
they can distort or modify biometric features, severely affecting the
capability of recognizing individuals' identity or even detecting the face.
Accordingly, we address the effect of such filters on the accuracy of automated
face detection and recognition. The social media image filters studied either
modify the image contrast or illumination or occlude parts of the face with for
example artificial glasses or animal noses. We observe that the effect of some
of these filters is harmful both to face detection and identity recognition,
specially if they obfuscate the eye or (to a lesser extent) the nose. To
counteract such effect, we develop a method to reconstruct the applied
manipulation with a modified version of the U-NET segmentation network. This is
observed to contribute to a better face detection and recognition accuracy.
From a recognition perspective, we employ distance measures and trained machine
learning algorithms applied to features extracted using a ResNet-34 network
trained to recognize faces. We also evaluate if incorporating filtered images
to the training set of machine learning approaches are beneficial for identity
recognition. Our results show good recognition when filters do not occlude
important landmarks, specially the eyes (identification accuracy >99%, EER<2%).
The combined effect of the proposed approaches also allow to mitigate the
effect produced by filters that occlude parts of the face, achieving an
identification accuracy of >92% with the majority of perturbations evaluated,
and an EER <8%. Although there is room for improvement, when neither U-NET
reconstruction nor training with filtered images is applied, the accuracy with
filters that severely occlude the eye is 12% (EER)Comment: Published at Pattern Recognition Letters, 202
SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning
The widespread use of mobile devices for various digital services has created
a need for reliable and real-time person authentication. In this context,
facial recognition technologies have emerged as a dependable method for
verifying users due to the prevalence of cameras in mobile devices and their
integration into everyday applications. The rapid advancement of deep
Convolutional Neural Networks (CNNs) has led to numerous face verification
architectures. However, these models are often large and impractical for mobile
applications, reaching sizes of hundreds of megabytes with millions of
parameters. We address this issue by developing SqueezerFaceNet, a light face
recognition network which less than 1M parameters. This is achieved by applying
a network pruning method based on Taylor scores, where filters with small
importance scores are removed iteratively. Starting from an already small
network (of 1.24M) based on SqueezeNet, we show that it can be further reduced
(up to 40%) without an appreciable loss in performance. To the best of our
knowledge, we are the first to evaluate network pruning methods for the task of
face recognition.Comment: Published at VIII International Workshop on Artificial Intelligence
and Pattern Recognition, IWAIPR 202
Periocular Biometrics: A Modality for Unconstrained Scenarios
Periocular refers to the region of the face that surrounds the eye socket.
This is a feature-rich area that can be used by itself to determine the
identity of an individual. It is especially useful when the iris or the face
cannot be reliably acquired. This can be the case of unconstrained or
uncooperative scenarios, where the face may appear partially occluded, or the
subject-to-camera distance may be high. However, it has received revived
attention during the pandemic due to masked faces, leaving the ocular region as
the only visible facial area, even in controlled scenarios. This paper
discusses the state-of-the-art of periocular biometrics, giving an overall
framework of its most significant research aspects
Eigen-patch iris super-resolution for iris recognition improvement
Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation.peer-reviewe
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