Redwan Sony

Iris-based biometric systems are vulnerable to presentation attacks (PAs), where adversaries present physical artifacts (e.g., printed iris images, textured contact lenses) to defeat the system. This has led to the development of various presentation attack detection (PAD) algorithms, which have demonstrated good performance in intra-domain settings. However, they often struggle to generalize effectively in cross-domain scenarios, where training and testing employ different sensors, PA instruments, and datasets. In this work, we use adversarial training samples of both bonafide irides and PAs to improve the cross-domain performance of a PAD classifier. The novelty lies in the method used to generate these samples. The proposed method leverages the transformation parameters used by classical data augmentation schemes (e.g., translation, rotation, shear). This is accomplished via a convolutional autoencoder, ADV-GEN, that utilizes original training samples along with a set of geometric and photometric transformations to produce adversarial samples. The transformation parameters act as regularization variables to guide ADV-GEN toward generating adversarial samples, which are then used in the training of a PAD classifier. Experiments conducted on the LivDet-Iris 2017 database, comprising four datasets, demonstrate the efficacy of this method in cross-domain scenarios.

Deep neural networks (DNNs) exhibit superior performance in various machine learning tasks, e.g., image classification, speech recognition, biometric recognition, object detection, etc. However, it is essential to analyze their sensitivity to parameter perturbations before deploying them in real-world applications. In this work, we assess the sensitivity of DNNs against perturbations to their weight and bias parameters. The sensitivity analysis involves three DNN architectures (VGG, ResNet, and DenseNet), three types of parameter perturbations (Gaussian noise, weight zeroing, and weight scaling), and two settings (entire network and layer-wise). We perform experiments in the context of iris presentation attack detection and evaluate on two publicly available datasets: LivDet-Iris-2017 and LivDet-Iris-2020. Based on the sensitivity analysis, we propose improved models simply by perturbing parameters of the network without undergoing training. We further combine these perturbed models at the score-level and at the parameter-level to improve the performance over the original model. The ensemble at the parameter-level shows an average improvement of 43.58% on the LivDet-Iris-2017 dataset and 9.25% on the LivDet-Iris-2020 dataset.

To ensure global food security and the overall profit of stakeholders, the importance of correctly detecting and classifying plant diseases is paramount. In this connection, the emergence of deep learning-based image classification has introduced a substantial number of solutions. However, the applicability of these solutions in low-end devices requires fast, accurate, and computationally inexpensive systems. This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. Our system extracts features using a combined model consisting of a pretrained MobileNetV2 architecture and a classifier network for effective prediction. Traditional augmentation approaches are replaced by runtime augmentation to avoid data leakage and address the class imbalance issue. Evaluation on tomato leaf images from the PlantVillage dataset shows that the proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations, making it a suitable choice for low-end devices.

Handwritten Signature is a widely used biometric in daily life as a mean of identity verification of an individual. For offline signature verification both accuracy and speed are important parameters. Accuracy may vary as the samples from signature datasets show a high intra-class variability. As these properties depend on the feature descriptor taken to represent the signature image, this selection is very important. In this study we provide a comparative performance evaluation of wellknown histogram based descriptors like SIFT and SURF and a wide variety of binary descriptors like BRIEF, ORB, BRISK and FREAK in the application of handwritten signature verification. We compare the performance of these feature descriptors against speed and accuracy. After the experimental analysis we have observed that binary features like ORB is faster with moderate accuracy but SIFT-like descriptors give better accuracy. Among them the combination of FAST feature detection and BRIEF descriptor is the fastest one but with lowest accuracy.