arXiv subjects

Siamul Karim Khan

Publications and source records attributed to Siamul Karim Khan.

6 recordsLinked to original sources

arXiv2026

EyePreserve: Identity-Preserving Iris Synthesis

Synthesis of same-identity biometric iris images, both for existing and non-existing identities, while preserving the identity across a wide range of pupil sizes, is complex due to the intricate iris muscle constriction mechanism, requiring a precise model of iris non-linear texture deformations to be embedded into the synthesis pipeline. This paper presents EyePreserve, a novel, fully data-driven non-linear texture deformation model, embedded within an identity-preserving, pupil size-varying synthesis framework. This approach is capable of synthesizing images of irises with different pupil sizes representing non-existing identities, as well as non-linearly deforming the texture of iris images of existing subjects, given the segmentation mask of the target iris image. Iris recognition experiments suggest that the proposed deformation model both preserves the identity when changing the pupil size, and offers better similarity between same-identity iris samples with significant differences in pupil size, compared to state-of-the-art linear and non-linear (biomechanical-based) iris deformation models. Two immediate applications of the proposed approach are: (a) synthesis of new, or enhancement of the existing datasets of same-identity iris images with varying pupil sizes, with correct modeling of complex iris texture deformations, and (b) helping forensic human experts examine iris image pairs with significant differences in pupil dilation by deforming one or both images to align the pupil size. Images considered in this work conform to selected ISO/IEC 29794-6 quality metrics to make them applicable in biometric systems. The source codes and model weights are offered with this paper.

cs.CV↗

arXiv2026

Lowering the Barrier to IREX Participation: Open-Source Algorithms, Toolkit, and Benchmarking for Iris Recognition

NIST Iris Exchange (IREX) offers an appealing solution to evaluating new open-source iris recognition algorithms, but it presents high barriers to entry because these algorithms must be written in C++, using a specific API, and adapted to meet strict IREX speed and memory constraints. The main goal of this paper is to lower these barriers and advance open-source iris recognition large-scale evaluations by offering: (a) two new modern deep learning-based open-source iris matchers (ArcIris and TripletIris), along with their C++ IREX X-compliant implementations, which are the first open-source iris recognition methods included into the IREX X leaderboard (and thus IREX-vetted), as well as new segmentation and iris circular approximation models that can be incorporated into any new iris recognition method, and (b) a performance assessment (according to IREX X testing protocols) of all major and currently available open-source iris recognition solutions. The paper also provides Python implementations of the new ArcIris and TripletIris methods and discusses the differences one may encounter between C++ and Python implementations of the same conceptually equivalent approaches. Finally, the paper offers open-source, IREX X-compliant C++ implementations of two existing methods: (a) an iris image filtering-based algorithm utilizing human saliency-driven kernels (HDBIF), and (b) a human-interpretable algorithm for detecting and comparing Fuchs' crypts (CRYPTS). In addition to IREX X evaluation results, the paper reports the performance of all methods on major academic benchmarks: Quality-Face/Iris Research Ensemble (Q-FIRE), Warsaw-Biobase Post-Mortem Iris, CASIA-Iris-Thousand-V4, CASIA-Iris-Lamp-V4, IIT Delhi Iris Database, IIITD Contact Lens Iris Database, NDIris3D, and Notre Dame Variable Iris Image Quality Release 2 (VII-Q-R2).

cs.CV↗

arXiv2026

Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations

Developing reliable iris recognition and presentation attack detection methods requires diverse datasets that capture realistic variations in iris features and a wide spectrum of anomalies. Because of the rich texture of iris images, which spans a wide range of spatial frequencies, synthesizing same-identity iris images while controlling specific attributes remains challenging. In this work, we introduce a new iris image augmentation strategy by traversing a generative model's latent space toward latent codes that represent same-identity samples but with some desired iris image properties manipulated. The latent space traversal is guided by a gradient of specific geometrical, textural, or quality-related iris image features (e.g., sharpness, pupil size, iris size, or pupil-to-iris ratio) and preserves the identity represented by the image being manipulated. The proposed approach can be easily extended to manipulate any attribute for which a differentiable loss term can be formulated. Additionally, our approach can use either randomly generated images using either a pre-train GAN model or real-world iris images. We can utilize GAN inversion to project any given iris image into the latent space and obtain its corresponding latent code.

cs.CV↗

arXiv2022

DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation

Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition. In dominant approaches to iris recognition, the size of a ring-shaped iris region is linearly scaled to a canonical rectangle, used further in encoding and matching. However, the biological complexity of the iris sphincter and dilator muscles causes the movements of iris features to be nonlinear in a function of pupil size, and not solely organized along radial paths. Alternatively to the existing theoretical models based on the biomechanics of iris musculature, in this paper we propose a novel deep autoencoder-based model that can effectively learn complex movements of iris texture features directly from the data. The proposed model takes two inputs, (a) an ISO-compliant near-infrared iris image with initial pupil size, and (b) the binary mask defining the target shape of the iris. The model makes all the necessary nonlinear deformations to the iris texture to match the shape of the iris in an image (a) with the shape provided by the target mask (b). The identity-preservation component of the loss function helps the model in finding deformations that preserve identity and not only the visual realism of the generated samples. We also demonstrate two immediate applications of this model: better compensation for iris texture deformations in iris recognition algorithms, compared to linear models, and the creation of a generative algorithm that can aid human forensic examiners, who may need to compare iris images with a large difference in pupil dilation. We offer the source codes and model weights available along with this paper.

cs.CV↗

arXiv2019

A CNN-based approach to classify cricket bowlers based on their bowling actions

With the advances in hardware technologies and deep learning techniques, it has become feasible to apply these techniques in diverse fields. Convolutional Neural Network (CNN), an architecture from the field of deep learning, has revolutionized Computer Vision. Sports is one of the avenues in which the use of computer vision is thriving. Cricket is a complex game consisting of different types of shots, bowling actions and many other activities. Every bowler, in a game of cricket, bowls with a different bowling action. We leverage this point to identify different bowlers. In this paper, we have proposed a CNN model to identify eighteen different cricket bowlers based on their bowling actions using transfer learning. Additionally, we have created a completely new dataset containing 8100 images of these eighteen bowlers to train the proposed framework and evaluate its performance. We have used the VGG16 model pre-trained with the ImageNet dataset and added a few layers on top of it to build our model. After trying out different strategies, we found that freezing the weights for the first 14 layers of the network and training the rest of the layers works best. Our approach achieves an overall average accuracy of 93.3% on the test set and converges to a very low cross-entropy loss.

cs.CV↗

arXiv2019

HishabNet: Detection, Localization and Calculation of Handwritten Bengali Mathematical Expressions

Recently, recognition of handwritten Bengali letters and digits have captured a lot of attention among the researchers of the AI community. In this work, we propose a Convolutional Neural Network (CNN) based object detection model which can recognize and evaluate handwritten Bengali mathematical expressions. This method is able to detect multiple Bengali digits and operators and locate their positions in the image. With that information, it is able to construct numbers from series of digits and perform mathematical operations on them. For the object detection task, the state-of-the-art YOLOv3 algorithm was utilized. For training and evaluating the model, we have engineered a new dataset 'Hishab' which is the first Bengali handwritten digits dataset intended for object detection. The model achieved an overall validation mean average precision (mAP) of 98.6%. Also, the classification accuracy of the feature extractor backbone CNN used in our model was tested on two publicly available Bengali handwritten digits datasets: NumtaDB and CMATERdb. The backbone CNN achieved a test set accuracy of 99.6252% on NumtaDB and 99.0833% on CMATERdb.

cs.CV↗