Corruption depth: Analysis of DNN depth for misclassification.

Many large and complex deep neural networks have been shown to provide higher performance on various computer vision tasks. However, very little is known about the relationship between the complexity of the input data along with the type of noise and the depth needed for correct classification. Existing studies do not address the issue of common corruptions adequately, especially in understanding what impact these corruptions leave on the individual part of a deep neural network.

AI-based radiodiagnosis using chest X-rays: A review.

Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide.

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review.

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development.

A novel abnormality annotation database for COVID-19 affected frontal lung X-rays.

Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients.

Crafting Adversarial Perturbations via Transformed Image Component Swapping.

Adversarial attacks have been demonstrated to fool the deep classification networks. There are two key characteristics of these attacks: firstly, these perturbations are mostly additive noises carefully crafted from the deep neural network itself. Secondly, the noises are added to the whole image, not considering them as the combination of multiple components from which they are made.

Boosting Face Presentation Attack Detection in Multi-Spectral Videos Through Score Fusion of Wavelet Partition Images.

Presentation attack detection (PAD) algorithms have become an integral requirement for the secure usage of face recognition systems. As face recognition algorithms and applications increase from constrained to unconstrained environments and in multispectral scenarios, presentation attack detection algorithms must also increase their scope and effectiveness. It is important to realize that the PAD algorithms are not only effective for one environment or condition but rather be generalizable to a multitude of variabilities that are presented to a face recognition algorithm.

MagNet: Detecting Digital Presentation Attacks on Face Recognition.

Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered.

Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images.

With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster.

Kernelized Heterogeneity-Aware Cross-View Face Recognition.

Cross-view or heterogeneous face matching involves comparing two different views of the face modality such as two different spectrums or resolutions. In this research, we present two heterogeneity-aware subspace techniques, heterogeneous discriminant analysis (HDA) and its kernel version (KHDA) that encode heterogeneity in the objective function and yield a suitable projection space for improved performance. They can be applied on any feature to make it heterogeneity invariant.

DeriveNet for (Very) Low Resolution Image Classification.

Images captured from a distance often result in (very) low resolution (VLR/LR) region of interest, requiring automated identification. VLR/LR images (or regions of interest) often contain less information content, rendering ineffective feature extraction and classification. To this effect, this research proposes a novel DeriveNet model for VLR/LR classification, which focuses on learning effective class boundaries by utilizing the class-specific domain knowledge.

DAMAD: Database, Attack, and Model Agnostic Adversarial Perturbation Detector.

Adversarial perturbations have demonstrated the vulnerabilities of deep learning algorithms to adversarial attacks. Existing adversary detection algorithms attempt to detect the singularities; however, they are in general, loss-function, database, or model dependent. To mitigate this limitation, we propose DAMAD-a generalized perturbation detection algorithm which is agnostic to model architecture, training data set, and loss function used during training.

Subgroup Invariant Perturbation for Unbiased Pre-Trained Model Prediction.

Modern deep learning systems have achieved unparalleled success and several applications have significantly benefited due to these technological advancements. However, these systems have also shown vulnerabilities with strong implications on the fairness and trustability of such systems. Among these vulnerabilities, bias has been an .

Phacoemulsification Cataract Surgery Affects the Discriminative Capacity of Iris Pattern Recognition.

Cataract is a common ophthalmic disorder and the leading cause of blindness worldwide. While cataract is cured via surgical procedures, its impact on iris based biometric recognition has not been effectively studied. The key objective of this research is to assess the effect of cataract surgery on the iris texture pattern as a means of personal authentication.

Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos.

Identifying kinship relations has garnered interest due to several applications such as organizing and tagging the enormous amount of videos being uploaded on the Internet. Existing research in kinship verification primarily focuses on kinship prediction with image pairs. In this research, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE).

Hierarchical Representation Learning for Kinship Verification.

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. In this paper, first, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determine their ability to recognize kin relationship using the whole face as well as specific facial regions.

Face Verification via Class Sparsity Based Supervised Encoding.

Autoencoders are deep learning architectures that learn feature representation by minimizing the reconstruction error. Using an autoencoder as baseline, this paper presents a novel formulation for a class sparsity based supervised encoder, termed as CSSE. We postulate that features from the same class will have a common sparsity pattern/support in the latent space.

Recognizing age-separated face images: humans and machines.

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms.

Improving cross-resolution face matching using ensemble-based co-transfer learning.

Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images.

Recognizing disguised faces: human and machine evaluation.

Face verification, though an easy task for humans, is a long-standing open research area. This is largely due to the challenging covariates, such as disguise and aging, which make it very hard to accurately verify the identity of a person. This paper investigates human and machine performance for recognizing/verifying disguised faces.

FR-CAPTCHA: CAPTCHA based on recognizing human faces.

A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is designed to distinguish humans from machines. Most of the existing tests require reading distorted text embedded in a background image. However, many existing CAPTCHAs are either too difficult for humans due to excessive distortions or are trivial for automated algorithms to solve.

Does iris change over time?

Iris as a biometric identifier is assumed to be stable over a period of time. However, some researchers have observed that for long time lapse, the genuine match score distribution shifts towards the impostor score distribution and the performance of iris recognition reduces. The main purpose of this study is to determine if the shift in genuine scores can be attributed to aging or not.

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image.

Integrating image quality in 2nu-SVM biometric match score fusion.

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image.

A mosaicing scheme for pose-invariant face recognition.

Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image.

Enhancing security of fingerprints through contextual biometric watermarking.

This paper presents a novel digital watermarking technique using face and demographic text data as multiple watermarks for verifying the chain of custody and protecting the integrity of a fingerprint image. The watermarks are embedded in selected texture regions of a fingerprint image using discrete wavelet transform. Experimental results show that modifications in these locations are visually imperceptible and maintain the minutiae details.