arXiv subjects

Arun Ross

Publications and source records attributed to Arun Ross.

At least 37 records · Page 2Linked to original sources

arXiv2025

Benchmarking Foundation Models for Zero-Shot Biometric Tasks

The advent of foundation models, particularly Vision-Language Models (VLMs) and Multi-modal Large Language Models (MLLMs), has redefined the frontiers of artificial intelligence, enabling remarkable generalization across diverse tasks with minimal or no supervision. Yet, their potential in biometric recognition and analysis remains relatively underexplored. In this work, we introduce a comprehensive benchmark that evaluates the zero-shot and few-shot performance of state-of-the-art publicly available VLMs and MLLMs across six biometric tasks spanning the face and iris modalities: face verification, soft biometric attribute prediction (gender and race), iris recognition, presentation attack detection (PAD), and face manipulation detection (morphs and deepfakes). A total of 41 VLMs were used in this evaluation. Experiments show that embeddings from these foundation models can be used for diverse biometric tasks with varying degrees of success. For example, in the case of face verification, a True Match Rate (TMR) of 96.77 percent was obtained at a False Match Rate (FMR) of 1 percent on the Labeled Face in the Wild (LFW) dataset, without any fine-tuning. In the case of iris recognition, the TMR at 1 percent FMR on the IITD-R-Full dataset was 97.55 percent without any fine-tuning. Further, we show that applying a simple classifier head to these embeddings can help perform DeepFake detection for faces, Presentation Attack Detection (PAD) for irides, and extract soft biometric attributes like gender and ethnicity from faces with reasonably high accuracy. This work reiterates the potential of pretrained models in achieving the long-term vision of Artificial General Intelligence.

cs.CV↗

arXiv2025

Shielding Latent Face Representations From Privacy Attacks

In today's data-driven analytics landscape, deep learning has become a powerful tool, with latent representations, known as embeddings, playing a central role in several applications. In the face analytics domain, such embeddings are commonly used for biometric recognition (e.g., face identification). However, these embeddings, or templates, can inadvertently expose sensitive attributes such as age, gender, and ethnicity. Leaking such information can compromise personal privacy and affect civil liberty and human rights. To address these concerns, we introduce a multi-layer protection framework for embeddings. It consists of a sequence of operations: (a) encrypting embeddings using Fully Homomorphic Encryption (FHE), and (b) hashing them using irreversible feature manifold hashing. Unlike conventional encryption methods, FHE enables computations directly on encrypted data, allowing downstream analytics while maintaining strong privacy guarantees. To reduce the overhead of encrypted processing, we employ embedding compression. Our proposed method shields latent representations of sensitive data from leaking private attributes (such as age and gender) while retaining essential functional capabilities (such as face identification). Extensive experiments on two datasets using two face encoders demonstrate that our approach outperforms several state-of-the-art privacy protection methods.

cs.CR↗

arXiv2025

Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait

We address the problem of whole-body person recognition in unconstrained environments. This problem arises in surveillance scenarios such as those in the IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) program, where biometric data is captured at long standoff distances, elevated viewing angles, and under adverse atmospheric conditions (e.g., turbulence and high wind velocity). To this end, we propose FarSight, a unified end-to-end system for person recognition that integrates complementary biometric cues across face, gait, and body shape modalities. FarSight incorporates novel algorithms across four core modules: multi-subject detection and tracking, recognition-aware video restoration, modality-specific biometric feature encoding, and quality-guided multi-modal fusion. These components are designed to work cohesively under degraded image conditions, large pose and scale variations, and cross-domain gaps. Extensive experiments on the BRIAR dataset, one of the most comprehensive benchmarks for long-range, multi-modal biometric recognition, demonstrate the effectiveness of FarSight. Compared to our preliminary system, this system achieves a 34.1% absolute gain in 1:1 verification accuracy (TAR@0.1% FAR), a 17.8% increase in closed-set identification (Rank-20), and a 34.3% reduction in open-set identification errors (FNIR@1% FPIR). Furthermore, FarSight was evaluated in the 2025 NIST RTE Face in Video Evaluation (FIVE), which conducts standardized face recognition testing on the BRIAR dataset. These results establish FarSight as a state-of-the-art solution for operational biometric recognition in challenging real-world conditions.

cs.CV↗

arXiv2025

A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection

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 typically perform well 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 of our approach lies in leveraging transformation parameters from classical data augmentation schemes (e.g., translation, rotation) to generate adversarial samples. We achieve this through a convolutional autoencoder, ADV-GEN, that inputs original training samples along with a set of geometric and photometric transformations. The transformation parameters act as regularization variables, guiding ADV-GEN to generate adversarial samples in a constrained search space. Experiments conducted on the LivDet-Iris 2017 database, comprising four datasets, and the LivDet-Iris 2020 dataset, demonstrate the efficacy of our proposed method. The code is available at https://github.com/iPRoBe-lab/ADV-GEN-IrisPAD.

cs.CV↗

arXiv2025

Task-conditioned Ensemble of Expert Models for Continuous Learning

One of the major challenges in machine learning is maintaining the accuracy of the deployed model (e.g., a classifier) in a non-stationary environment. The non-stationary environment results in distribution shifts and, consequently, a degradation in accuracy. Continuous learning of the deployed model with new data could be one remedy. However, the question arises as to how we should update the model with new training data so that it retains its accuracy on the old data while adapting to the new data. In this work, we propose a task-conditioned ensemble of models to maintain the performance of the existing model. The method involves an ensemble of expert models based on task membership information. The in-domain models-based on the local outlier concept (different from the expert models) provide task membership information dynamically at run-time to each probe sample. To evaluate the proposed method, we experiment with three setups: the first represents distribution shift between tasks (LivDet-Iris-2017), the second represents distribution shift both between and within tasks (LivDet-Iris-2020), and the third represents disjoint distribution between tasks (Split MNIST). The experiments highlight the benefits of the proposed method. The source code is available at https://github.com/iPRoBe-lab/Continuous_Learning_FE_DM.

cs.LG↗

arXiv2025

Detecting Near-Duplicate Face Images

Near-duplicate images are often generated when applying repeated photometric and geometric transformations that produce imperceptible variants of the original image. Consequently, a deluge of near-duplicates can be circulated online posing copyright infringement concerns. The concerns are more severe when biometric data is altered through such nuanced transformations. In this work, we address the challenge of near-duplicate detection in face images by, firstly, identifying the original image from a set of near-duplicates and, secondly, deducing the relationship between the original image and the near-duplicates. We construct a tree-like structure, called an Image Phylogeny Tree (IPT) using a graph-theoretic approach to estimate the relationship, i.e., determine the sequence in which they have been generated. We further extend our method to create an ensemble of IPTs known as Image Phylogeny Forests (IPFs). We rigorously evaluate our method to demonstrate robustness across other modalities, unseen transformations by latest generative models and IPT configurations, thereby significantly advancing the state-of-the-art performance by 42% on IPF reconstruction accuracy.

cs.CV↗

arXiv2025

Face Deepfakes -- A Comprehensive Review

In recent years, remarkable advancements in deep-fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.

cs.CV↗

arXiv2025

Metric for Evaluating Performance of Reference-Free Demorphing Methods

A facial morph is an image created by combining two (or more) face images pertaining to two (or more) distinct identities. Reference-free face demorphing inverts the process and tries to recover the face images constituting a facial morph without using any other information. However, there is no consensus on the evaluation metrics to be used to evaluate and compare such demorphing techniques. In this paper, we first analyze the shortcomings of the demorphing metrics currently used in the literature. We then propose a new metric called biometrically cross-weighted IQA that overcomes these issues and extensively benchmark current methods on the proposed metric to show its efficacy. Experiments on three existing demorphing methods and six datasets on two commonly used face matchers validate the efficacy of our proposed metric.

cs.CV↗

arXiv2025

Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network

Plant phenology and phenotype prediction using remote sensing data are increasingly gaining attention within the plant science community as a promising approach to enhance agricultural productivity. This work focuses on generating synthetic forestry images that satisfy certain phenotypic attributes, viz. canopy greenness. We harness a Generative Adversarial Network (GAN) to synthesize biologically plausible and phenotypically stable forestry images conditioned on the greenness of vegetation (a continuous attribute) over a specific region of interest, describing a particular vegetation type in a mixed forest. The training data is based on the automated digital camera imagery provided by the National Ecological Observatory Network (NEON) and processed by the PhenoCam Network. Our method helps render the appearance of forest sites specific to a greenness value. The synthetic images are subsequently utilized to predict another phenotypic attribute, viz., redness of plants. The quality of the synthetic images is assessed using the Structural SIMilarity (SSIM) index and Fréchet Inception Distance (FID). Further, the greenness and redness indices of the synthetic images are compared against those of the original images using Root Mean Squared Percentage Error (RMSPE) to evaluate their accuracy and integrity. The generalizability and scalability of our proposed GAN model are established by effectively transforming it to generate synthetic images for other forest sites and vegetation types. From a broader perspective, this approach could be leveraged to visualize forestry based on different phenotypic attributes in the context of various environmental parameters.

cs.CV↗

arXiv2024

Beyond the Visible: A Survey on Cross-spectral Face Recognition

Cross-spectral face recognition (CFR) refers to recognizing individuals using face images stemming from different spectral bands, such as infrared versus visible. While CFR is inherently more challenging than classical face recognition due to significant variation in facial appearance caused by the modality gap, it is useful in many scenarios including night-vision biometrics and detecting presentation attacks. Recent advances in deep neural networks (DNNs) have resulted in significant improvement in the performance of CFR systems. Given these developments, the contributions of this survey are three-fold. First, we provide an overview of CFR, by formalizing the CFR problem and presenting related applications. Secondly, we discuss the appropriate spectral bands for face recognition and discuss recent CFR methods, placing emphasis on deep neural networks. In particular we describe techniques that have been proposed to extract and compare heterogeneous features emerging from different spectral bands. We also discuss the datasets that have been used for evaluating CFR methods. Finally, we discuss the challenges and future lines of research on this topic.

cs.CV↗

arXiv2024

On Missing Scores in Evolving Multibiometric Systems

The use of multiple modalities (e.g., face and fingerprint) or multiple algorithms (e.g., three face comparators) has shown to improve the recognition accuracy of an operational biometric system. Over time a biometric system may evolve to add new modalities, retire old modalities, or be merged with other biometric systems. This can lead to scenarios where there are missing scores corresponding to the input probe set. Previous work on this topic has focused on either the verification or identification tasks, but not both. Further, the proportion of missing data considered has been less than 50%. In this work, we study the impact of missing score data for both the verification and identification tasks. We show that the application of various score imputation methods along with simple sum fusion can improve recognition accuracy, even when the proportion of missing scores increases to 90%. Experiments show that fusion after score imputation outperforms fusion with no imputation. Specifically, iterative imputation with K nearest neighbors consistently surpasses other imputation methods in both the verification and identification tasks, regardless of the amount of scores missing, and provides imputed values that are consistent with the ground truth complete dataset.

cs.CV↗

arXiv2024

Facial Demorphing via Identity Preserving Image Decomposition

A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby undermining the security of a face recognition system. To deal with this problem, several morph attack detection techniques have been developed. But these methods do not extract any information about the underlying bonafides used to create them. Demorphing addresses this limitation. However, current demorphing techniques are mostly reference-based, i.e, they need an image of one of the identities to recover the other. In this work, we treat demorphing as an ill-posed decomposition problem. We propose a novel method that is reference-free and recovers the bonafides with high accuracy. Our method decomposes the morph into several identity-preserving feature components. A merger network then weighs and combines these components to recover the bonafides. Our method is observed to reconstruct high-quality bonafides in terms of definition and fidelity. Experiments on the CASIA-WebFace, SMDD and AMSL datasets demonstrate the effectiveness of our method.

cs.CV↗

arXiv2024

To Impute or Not: Recommendations for Multibiometric Fusion

Combining match scores from different biometric systems via fusion is a well-established approach to improving recognition accuracy. However, missing scores can degrade performance as well as limit the possible fusion techniques that can be applied. Imputation is a promising technique in multibiometric systems for replacing missing data. In this paper, we evaluate various score imputation approaches on three multimodal biometric score datasets, viz. NIST BSSR1, BIOCOP2008, and MIT LL Trimodal, and investigate the factors which might influence the effectiveness of imputation. Our studies reveal three key observations: (1) Imputation is preferable over not imputing missing scores, even when the fusion rule does not require complete score data. (2) Balancing the classes in the training data is crucial to mitigate negative biases in the imputation technique towards the under-represented class, even if it involves dropping a substantial number of score vectors. (3) Multivariate imputation approaches seem to be beneficial when scores between modalities are correlated, while univariate approaches seem to benefit scenarios where scores between modalities are less correlated.

cs.CV↗

arXiv2024

ChatGPT Meets Iris Biometrics

This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition - a field less common and more specialized than face recognition. By focusing on this niche yet crucial area, we investigate how well AI tools like ChatGPT can understand and analyze iris images. Through a series of meticulously designed experiments employing a zero-shot learning approach, the capabilities of ChatGPT-4 was assessed across various challenging conditions including diverse datasets, presentation attacks, occlusions such as glasses, and other real-world variations. The findings convey ChatGPT-4's remarkable adaptability and precision, revealing its proficiency in identifying distinctive iris features, while also detecting subtle effects like makeup on iris recognition. A comparative analysis with Gemini Advanced - Google's AI model - highlighted ChatGPT-4's better performance and user experience in complex iris analysis tasks. This research not only validates the use of LLMs for specialized biometric applications but also emphasizes the importance of nuanced query framing and interaction design in extracting significant insights from biometric data. Our findings suggest a promising path for future research and the development of more adaptable, efficient, robust and interactive biometric security solutions.

cs.CV↗

arXiv2024

Iris-SAM: Iris Segmentation Using a Foundation Model

Iris segmentation is a critical component of an iris biometric system and it involves extracting the annular iris region from an ocular image. In this work, we develop a pixel-level iris segmentation model from a foundational model, viz., Segment Anything Model (SAM), that has been successfully used for segmenting arbitrary objects. The primary contribution of this work lies in the integration of different loss functions during the fine-tuning of SAM on ocular images. In particular, the importance of Focal Loss is borne out in the fine-tuning process since it strategically addresses the class imbalance problem (i.e., iris versus non-iris pixels). Experiments on ND-IRIS-0405, CASIA-Iris-Interval-v3, and IIT-Delhi-Iris datasets convey the efficacy of the trained model for the task of iris segmentation. For instance, on the ND-IRIS-0405 dataset, an average segmentation accuracy of 99.58% was achieved, compared to the best baseline performance of 89.75%.

cs.CV↗

arXiv2024

Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis

Biometric systems based on iris recognition are currently being used in border control applications and mobile devices. However, research in iris recognition is stymied by various factors such as limited datasets of bonafide irides and presentation attack instruments; restricted intra-class variations; and privacy concerns. Some of these issues can be mitigated by the use of synthetic iris data. In this paper, we present a comprehensive review of state-of-the-art GAN-based synthetic iris image generation techniques, evaluating their strengths and limitations in producing realistic and useful iris images that can be used for both training and testing iris recognition systems and presentation attack detectors. In this regard, we first survey the various methods that have been used for synthetic iris generation and specifically consider generators based on StyleGAN, RaSGAN, CIT-GAN, iWarpGAN, StarGAN, etc. We then analyze the images generated by these models for realism, uniqueness, and biometric utility. This comprehensive analysis highlights the pros and cons of various GANs in the context of developing robust iris matchers and presentation attack detectors.

cs.CV↗

arXiv2024

A Linguistic Comparison between Human and ChatGPT-Generated Conversations

This study explores linguistic differences between human and LLM-generated dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to the EmpathicDialogues dataset. The research employs Linguistic Inquiry and Word Count (LIWC) analysis, comparing ChatGPT-generated conversations with human conversations across 118 linguistic categories. Results show greater variability and authenticity in human dialogues, but ChatGPT excels in categories such as social processes, analytical style, cognition, attentional focus, and positive emotional tone, reinforcing recent findings of LLMs being "more human than human." However, no significant difference was found in positive or negative affect between ChatGPT and human dialogues. Classifier analysis of dialogue embeddings indicates implicit coding of the valence of affect despite no explicit mention of affect in the conversations. The research also contributes a novel, companion ChatGPT-generated dataset of conversations between two independent chatbots, which were designed to replicate a corpus of human conversations available for open access and used widely in AI research on language modeling. Our findings enhance understanding of ChatGPT's linguistic capabilities and inform ongoing efforts to distinguish between human and LLM-generated text, which is critical in detecting AI-generated fakes, misinformation, and disinformation.

cs.CL↗

arXiv2024

Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption

Modern face recognition systems utilize deep neural networks to extract salient features from a face. These features denote embeddings in latent space and are often stored as templates in a face recognition system. These embeddings are susceptible to data leakage and, in some cases, can even be used to reconstruct the original face image. To prevent compromising identities, template protection schemes are commonly employed. However, these schemes may still not prevent the leakage of soft biometric information such as age, gender and race. To alleviate this issue, we propose a novel technique that combines Fully Homomorphic Encryption (FHE) with an existing template protection scheme known as PolyProtect. We show that the embeddings can be compressed and encrypted using FHE and transformed into a secure PolyProtect template using polynomial transformation, for additional protection. We demonstrate the efficacy of the proposed approach through extensive experiments on multiple datasets. Our proposed approach ensures irreversibility and unlinkability, effectively preventing the leakage of soft biometric attributes from face embeddings without compromising recognition accuracy.

cs.CR↗