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Anil Jain

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Computer Vision cond-mat.str-el cond-mat.mtrl-sci Machine Learning quant-ph

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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.

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.

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).

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.

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.

preprint2015arXiv

Similarity Learning via Adaptive Regression and Its Application to Image Retrieval

We study the problem of similarity learning and its application to image retrieval with large-scale data. The similarity between pairs of images can be measured by the distances between their high dimensional representations, and the problem of learning the appropriate similarity is often addressed by distance metric learning. However, distance metric learning requires the learned metric to be a PSD matrix, which is computational expensive and not necessary for retrieval ranking problem. On the other hand, the bilinear model is shown to be more flexible for large-scale image retrieval task, hence, we adopt it to learn a matrix for estimating pairwise similarities under the regression framework. By adaptively updating the target matrix in regression, we can mimic the hinge loss, which is more appropriate for similarity learning problem. Although the regression problem can have the closed-form solution, the computational cost can be very expensive. The computational challenges come from two aspects: the number of images can be very large and image features have high dimensionality. We address the first challenge by compressing the data by a randomized algorithm with the theoretical guarantee. For the high dimensional issue, we address it by taking low rank assumption and applying alternating method to obtain the partial matrix, which has a global optimal solution. Empirical studies on real world image datasets (i.e., Caltech and ImageNet) demonstrate the effectiveness and efficiency of the proposed method.

preprint2014arXiv

Competing exchange interactions on the verge of a metal-insulator transition in the two-dimensional spiral magnet Sr$_3$Fe$_2$O$_7$

We report a neutron scattering study of the magnetic order and dynamics of the bilayer perovskite Sr$_3$Fe$_2$O$_7$, which exhibits a temperature-driven metal-insulator transition at 340 K. We show that the Fe$^{4+}$ moments adopt incommensurate spiral order below $T_\text{N}=115$ K and provide a comprehensive description of the corresponding spin wave excitations. The observed magnetic order and excitation spectra can be well understood in terms of an effective spin Hamiltonian with interactions ranging up to third nearest-neighbor pairs. The results indicate that the helical magnetism in Sr$_3$Fe$_2$O$_7$ results from competition between ferromagnetic double-exchange and antiferromagnetic superexchange interactions whose strengths become comparable near the metal-insulator transition. They thus confirm a decades-old theoretical prediction and provide a firm experimental basis for models of magnetic correlations in strongly correlated metals.

preprint2013arXiv

One-Dimensional Dispersive Magnon Excitation in the Frustrated Spin-2 Chain System Ca3Co2O6

Using inelastic neutron scattering, we have observed a quasi-one-dimensional dispersive magnetic excitation in the frustrated triangular-lattice spin-2 chain oxide Ca3Co2O6. At the lowest temperature (T = 1.5 K), this magnon is characterized by a large zone-center spin gap of ~27 meV, which we attribute to the large single-ion anisotropy, and disperses along the chain direction with a bandwidth of ~3.5 meV. In the directions orthogonal to the chains, no measurable dispersion was found. With increasing temperature, the magnon dispersion shifts towards lower energies, yet persists up to at least 150 K, indicating that the ferromagnetic intrachain correlations survive up to 6 times higher temperatures than the long-range interchain antiferromagnetic order. The magnon dispersion can be well described within the predictions of linear spin-wave theory for a system of weakly coupled ferromagnetic chains with large single-ion anisotropy, enabling the direct quantitative determination of the magnetic exchange and anisotropy parameters.