BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Anil Jain

Anil Jain contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.mtrl-scicond-mat.str-elquant-ph

Trust snapshot

Quick read

Trust 19 - UnverifiedVerification L1Unclaimed author
5works
0followers
4topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

5 published item(s)

preprint2026arXiv

Non-Colliding Biometric Identities for Digital Entities: Geometry, Capacity, and Million-Scale Virtual Identity Provisioning

Digital entities such as AI agents and humanoid robots increasingly operate alongside real humans, yet their identity infrastructure is based on credentials rather than embodied biometric identity. We introduce Biometric Identity Provisioning (BIP), a new problem and solution framework that addresses: given an enrollment gallery of real human identities, provision virtual identities that are non-colliding with every enrolled identity, maintain sufficient inter-class separability, and are realizable as high-fidelity face images. The key geometric insight is that real face identities occupy a low-dimensional subspace of the embedding hypersphere, leaving no residual subspace for virtual identities. Hence, virtual identities must instead be allocated as unclaimed gaps within the real face manifold itself. BIP is therefore a constrained packing problem: available gaps vastly exceed any foreseeable enrollment scale, and provisioned identities remain non-colliding even as new real identities are subsequently enrolled. Grounded in this geometry, our repulsion-based allocation is not bounded by any fixed provisioning count; we demonstrate 10M non-colliding virtual identity embeddings against a gallery of 360K real identities. Realizing these embeddings as face images requires a generator that operates outside the training distribution of real face images; we introduce GapGen, a gap-aware generator trained with a curriculum that progressively extends synthesis into non-colliding regions, validated at 1M photorealistic virtual face images. We further construct v-LFW, a virtual counterpart to LFW face dataset, with protocols for virtual face verification, cross-reality matching, real-vs-virtual detection, and unified recognition and detection.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

On the Holistic Approach for Detecting Human Image Forgery

The rapid advancement of AI-generated content (AIGC) has escalated the threat of deepfakes, from facial manipulations to the synthesis of entire photorealistic human bodies. However, existing detection methods remain fragmented, specializing either in facial-region forgeries or full-body synthetic images, and consequently fail to generalize across the full spectrum of human image manipulations. We introduce HuForDet, a holistic framework for human image forgery detection, which features a dual-branch architecture comprising: (1) a face forgery detection branch that employs heterogeneous experts operating in both RGB and frequency domains, including an adaptive Laplacian-of-Gaussian (LoG) module designed to capture artifacts ranging from fine-grained blending boundaries to coarse-scale texture irregularities; and (2) a contextualized forgery detection branch that leverages a Multi-Modal Large Language Model (MLLM) to analyze full-body semantic consistency, enhanced with a confidence estimation mechanism that dynamically weights its contribution during feature fusion. We curate a human image forgery (HuFor) dataset that unifies existing face forgery data with a new corpus of full-body synthetic humans. Extensive experiments show that our HuForDet achieves state-of-the-art forgery detection performance and superior robustness across diverse human image forgeries.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Controllable and Guided Face Synthesis for Unconstrained Face Recognition

Although significant advances have been made in face recognition (FR), FR in unconstrained environments remains challenging due to the domain gap between the semi-constrained training datasets and unconstrained testing scenarios. To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets in a style latent space. CFSM learns a linear subspace with orthogonal bases in the style latent space with precise control over the diversity and degree of synthesis. Furthermore, the pre-trained synthesis model can be guided by the FR model, making the resulting images more beneficial for FR model training. Besides, target dataset distributions are characterized by the learned orthogonal bases, which can be utilized to measure the distributional similarity among face datasets. Our approach yields significant performance gains on unconstrained benchmarks, such as IJB-B, IJB-C, TinyFace and IJB-S (+5.76% Rank1).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Experimental realisation of multipartite entanglement via quantum Fisher information in a uniform antiferromagnetic quantum spin chain

Quantum entanglement is a quantum mechanical phenomenon where the quantum state of a many-body system with many degrees of freedom cannot be described independently of the state of each body with a given degree of freedom, no matter how far apart in space each body is. Entanglement is not only considered a resource in quantum information but also believed to affect complex condensed matter systems. Detecting and quantifying multi-particle entanglement in a many-body system is thus of fundamental significance for both quantum information science and condensed matter physics. Here, we detect and quantify multipartite entanglement in a spin 1/2 Heisenberg antiferromagnetic chain in a bulk solid. Multipartite entanglement was detected using quantum Fisher information which was obtained using dynamic susceptibility measured via inelastic neutron scattering. The scaling behaviour of quantum Fisher information was found to identify the spin 1/2 Heisenberg antiferromagnetic chain to belong to a class of strongly entangled quantum phase transitions with divergent multipartite entanglement.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Spin dynamics and unconventional magnetism in insulating La$_{(1-2x)}$Sr$_{2x}$Co$_{(1-x)}$Nb$_{x}$O$_3$

We study the structural, magnetic, transport and electronic properties of LaCoO$_3$ with Sr/Nb co-substitution, i.e., La$_{(1-2x)}$Sr$_{2x}$Co$_{(1-x)}$Nb$_{x}$O$_3$ using x-ray and neutron diffraction, dc and ac-magnetization, neutron depolarization, dc-resistivity and photoemission measurements. The powder x-ray and neutron diffraction data were fitted well with the rhombohedral crystal symmetry (space group \textit{R$\bar{3}$c}) in Rietveld refinement analysis. The calculated effective magnetic moment ($\approx$3.85~$μ_B$) and average spin ($\approx$1.5) of Co ions from the analysis of magnetic susceptibility data are consistent with 3+ state of Co ions in intermediate-spin (IS) and high-spin (HS) states in the ratio of $\approx$50:50, i.e., spin-state of Co$^{3+}$ is preserved at least up to $x=$ 0.1 sample. Interestingly, the magnetization values were significantly increased with respect to the $x=$ 0 sample, and the M-H curves show non-saturated behavior up to an applied maximum magnetic field of $\pm$70 kOe. The ac-susceptibility data show a shift in the freezing temperature with excitation frequency and the detailed analysis confirm the slower dynamics and a non-zero value of the Vogel-Fulcher temperature T$_0$, which suggests for the cluster spin glass. The unusual magnetic behavior indicates the presence of complex magnetic interactions at low temperatures. The dc-resistivity measurements show the insulating nature in all the samples. However, relatively large density of states $\approx$10$^{22}$ eV$^{-1}$cm$^{-3}$ and low activation energy $\approx$130~meV are found in $x=$ 0.05 sample. Using x-ray photoemission spectroscopy, we study the core-level spectra of La 3$d$, Co 2$p$, Sr 3$d$, and Nb 3$d$ to confirm the valence state.

0 citations0 reviewsNo code linkedOpen access