Trimodal Person Identification Framework

• The trimodal person identification framework is an integrated system combining three distinct biometric modalities with specialized encoders and deep neural architectures.
• It employs multi-level feature abstraction and advanced fusion techniques—such as weighted averaging, attention-based fusion, and mixture-of-experts—to synthesize comprehensive identity representations.
• The framework incorporates dynamic confidence-weighting and missing-modality losses to maintain robust performance even in the presence of incomplete or degraded data.

A trimodal person identification framework is an integrated system that simultaneously leverages three heterogeneous biometric or sensory modalities—such as face, voice, gesture, audio, visible/infrared/thermal imaging, text, sketch, or bio-signals—for robust and accurate individual recognition. Recent frameworks employ deep neural architectures, advanced fusion strategies, and explicit robustness mechanisms for missing or degraded modalities, achieving superior performance compared to unimodal and bimodal systems across large-scale and unconstrained environments.

1. Architectural Principles of Trimodal Person Identification

Several design paradigms are prevalent across the literature:

• Modality-Specific Encoding: Each modality (e.g., face, voice, gesture) is processed by a specialized encoder (e.g., ResNet, ViT, or Transformer-based branch), optimized for that data domain. For example, face crops may be processed by IR101-Adaface, voice through deep convolutional networks like ResNet293, and gestures via spatiotemporal architectures such as TimeSformer or 3D CNNs (Farhadipour et al., 16 Dec 2025, Soleymani et al., 2018, John et al., 2022, Ye et al., 2019, Zuo et al., 11 Jun 2025, Sun et al., 17 Oct 2025).
• Multi-Level Abstraction: Embeddings are extracted from different abstraction layers within a modality-specific encoder (e.g., after pool3 and pool5 blocks in VGG-19), capturing both shallow texture and deep semantic features. Modalities such as face, iris, and fingerprint can each contribute multi-scale representations that are projected to common-dimensional embeddings for subsequent fusion (Soleymani et al., 2018).
• Feature and Score Fusion Layers: After obtaining modality-specific embeddings, frameworks employ fusion mechanisms—ranging from simple weighted averaging, concatenation plus MLP, attention-based fusion, to dynamic gating or mixture-of-experts modules—to synthesize a comprehensive identity representation. Advanced systems implement cross-modal transformer blocks or cross-attention to model dependencies among modalities (Farhadipour et al., 16 Dec 2025, John et al., 2022, Sun et al., 17 Oct 2025, Zuo et al., 11 Jun 2025).
• Decision-Level Hybridization: In large-scale or unreliable conditions, decision-level fusion complements feature-level fusion. For example, a two-stage model might route high-quality samples to feature fusion, and hard/noisy samples to a decision-level ensemble integrating per-modal classifier outputs (Ye et al., 2019).

2. Fusion Techniques and Mathematical Formulations

Fusion is the critical operation in trimodal frameworks, dictating robustness and discriminative capacity. Representative strategies include:

Joint Feature Fusion

Given modality embeddings $f_m^{(l)} \in \mathbb{R}^d$ (modality $m$, abstraction level $l$), features are concatenated:

$$\mathbf{x} = [f_1^{(1)}; \dots; f_M^{(L)}] \in \mathbb{R}^{M \cdot L \cdot d}$$

and passed through a learned fusion MLP:

$$\mathbf{z} = \mathrm{ReLU}(W_f \mathbf{x} + b_f) \in \mathbb{R}^{d_{joint}}$$

Final classification is via softmax over $\mathbf{z}$ (Soleymani et al., 2018).

Adaptive Mixture-of-Experts (MoE)

Trimodal features are combined with data-driven weighting:

$$h = [f_\text{rgb}; f_\text{ir}; f_\text{sketch}] \ \boldsymbol{\alpha} = \mathrm{Softmax}(W_g h + b_g) \ f_\text{fused} = \sum_{m \in \{\text{rgb}, \text{ir}, \text{sketch}\}} \alpha_m E_m(f_m)$$

where $E_m$ is a modality-specific lightweight Adapter (Sun et al., 17 Oct 2025).

Cross-Attention Fusion

Each modality receives queries from the projections of other branch embeddings:

$$Q_f = W_q^f z^{(f)} \ K_f = W_k^f (\mathrm{proj}(z^{(g)}) + \mathrm{proj}(z^{(v)})) \ V_f = W_v^f (\mathrm{proj}(z^{(g)}) + \mathrm{proj}(z^{(v)})) \ A_f = \mathrm{softmax}\left(\frac{Q_f K_f^T}{\sqrt{d}}\right) \ z_x^{(f)} = A_f V_f$$

yielding cross-modally enhanced hidden representations (Farhadipour et al., 16 Dec 2025).

Score Fusion and Confidence-Weighted Averaging

Combined prediction logits across modalities, weighted by data-driven confidences:

$$p_\text{conf} = \frac{c_f^2 p^{(f)} + c_g^2 p^{(g)} + c_v^2 p^{(v)}}{c_f^2 + c_g^2 + c_v^2}$$

where $c_m$ is a learned confidence score per modality (Farhadipour et al., 16 Dec 2025).

3. Handling Missing or Degraded Modalities

Robustness to incomplete input is a central requirement:

• Confidence-Weighted Fusion dynamically reduces the influence of low-quality or missing streams by modulating weights as $c_m \to 0$ for absent data, ensuring the fused output defaults to the most reliable modalities (Farhadipour et al., 16 Dec 2025).
• Missing-Modality Losses in latent embedding spaces employ explicit prototype-repulsion constraints; e.g., a specialized loss penalizes embeddings that collapse towards a common point when a modality is missing:

$$\mathcal{L}_{z} = \ln(1 + \exp( d(\text{anchor},\text{hardest pos}) - d(\text{anchor},\text{hardest neg}) - d(\text{anchor},e^m) ))$$

where $e^m$ is a fixed missing-modality prototype (John et al., 2022).

• Hybrid Routing: High-quality (face-rich) samples are processed exclusively via the best-performing pathway, while hard cases activate all branches with ensemble decision fusion (Ye et al., 2019).

4. Performance Benchmarks and Empirical Results

Trimodal frameworks substantially outperform unimodal and bimodal baselines across public datasets and diverse sensing conditions:

Reference	Modalities	Dataset	Fusion Type	Top-1/Rank-1/Accuracy	Other Metrics
(Farhadipour et al., 16 Dec 2025)	Face, Voice, Gesture	CANDOR	Cross-attn, gated, conf-fused	99.18% (Trimodal)	Top-5: 99.58%
(Soleymani et al., 2018)	Face, Iris, Fingerprint	BioCop/BIOMDATA	Multi-level FFN	99.34%-99.91%	Param. savings O(10^8)
(John et al., 2022)	Audio, Visible, Thermal	Speaking Faces	AVTNet, missing-mod loss	99.39% avg.	Robust under missing
(Ye et al., 2019)	Face, Audio, Head	iQIYI-VID	Score/rank fusion	92.17% MAP	Decision-level hybrid
(Sun et al., 17 Oct 2025)	RGB, Infrared, Sketch	CIRS-PEDES	MoE, cross-modal attn	85.97% R-1, 81.02% mAP	Generalizes > biomodal
(Zuo et al., 11 Jun 2025)	RGB, IR, Text	ORBench	Tokenizer+MER+FM+SDM	93.53% (R-1), 82.83% mAP	-

Trimodal approaches yield significant relative error rate reductions: e.g., in the multimodal person verification scenario, fusing audio, visual and thermal yields 1.80% EER on clean test data—an 18% improvement over bimodal—while maintaining robustness under 30% synthetic corruption (Abdrakhmanova et al., 2021).

5. Application Scenarios and Benchmark Datasets

Frameworks are validated across forensic, surveillance, health, and unconstrained multimedia domains:

• Person Identification in Interviews or Surveillance: Utilizing face, voice, and gesture streams in video interviews (CANDOR) or cinematic data enables resilient and interpretable recognition—even when a subset of modalities is unavailable (Farhadipour et al., 16 Dec 2025, Ye et al., 2019).
• Large-Scale Biometric Auth: Face, fingerprint, and iris biometrics with VGG-based streams and multi-level abstraction achieve >99% identification across 300+ identities, with substantial parameter efficiency and consistent CMC curve superiority (Soleymani et al., 2018).
• Wearable Sensing and Privacy Attacks: Physiological (PPG, EDA) and physical (ACC/gesture) signals in mmSNN architectures demonstrate that individual-specific bio-signatures can be extracted from sensor data for re-identification, with up to 76% identification accuracy in cross-day/subject scenarios (Alam, 2021).
• Multi-Platform and Multi-Modality Re-ID: Urban-scale ReID systems covering RGB/IR/thermal imaging from ground and UAV platforms, with prompt-based cross-modal alignment, show consistent gains in cross-modality and cross-platform settings (MP-ReID, ORBench) (Ha et al., 21 Mar 2025, Zuo et al., 11 Jun 2025).

6. Open Challenges and Future Directions

While trimodal person identification frameworks have achieved state-of-the-art performance, several open issues remain:

• Scaling to More Modalities: Most systems handle at most three streams; scaling to “omnimodal” (>3) settings requires even more dynamic mixing and more sophisticated missing-modality compensation (Zuo et al., 11 Jun 2025).
• Fine-Grained Robustness: Hard zero-filling and “missing-modality prototypes” do not address real sensor failure distributions or adversarial cross-modal attack surfaces (John et al., 2022, Farhadipour et al., 16 Dec 2025).
• Generalization to In-the-Wild Benchmarks: Current datasets, though increasingly realistic, may not capture all deployment edge cases regarding environmental, subject, or sensor diversity (Ye et al., 2019, Ha et al., 21 Mar 2025).
• Online and Modular Reconfiguration: Practical deployment in sensor networks or multi-platform environments calls for frameworks that can be “hot-plugged” with dynamic modality sets, requiring new meta-learning or modular architecture paradigms (Sun et al., 17 Oct 2025, Zuo et al., 11 Jun 2025).

Future work envisions extensions to additional modalities (e.g., 3D skeleton, radar, or context-aware meta-sensors), dynamic weighting and gating architectures that learn in non-stationary environments, and more comprehensive modeling of uncertainty under multiple simultaneously missing or corrupted channels.

7. Summary Table of Key Trimodal Frameworks

System/Reference	Modalities	Fusion/Robustness Mechanisms	Top-1 / EER / MAP
(Farhadipour et al., 16 Dec 2025) Adaptive Multimodal	Face, Voice, Gesture	Cross-attn, gated, confidence-fused	99.18% (CANDOR)
(Soleymani et al., 2018) Multi-Level Abstraction	Face, Iris, Fingerprint	Multi-level joint fusion, end-to-end	99.34%–99.91% (BioCop/BIOMDATA)
(John et al., 2022) AVTNet	Audio, Visible, Thermal	Transformer fusion, missing-modality loss	99.39% Avg. (SpeakingFaces)
(Ye et al., 2019) Real Environments	Face, Audio, Head	Score/rank fusion, hybrid routing	92.17% MAP (iQIYI-VID)
(Sun et al., 17 Oct 2025) FlexiReID	RGB, IR, Sketch	MoE, cross-modal query fusion	85.97% R-1 (CUHK-PEDES)
(Alam, 2021) mmSNN	PPG, EDA, ACC (gesture)	Siamese metric + softmax fusion	~76% (26–28 subjs; PPG+ACC+EDA)

These frameworks collectively establish the technical and methodological foundations for high-precision, robust, and flexible trimodal person identification and re-identification in challenging, real-world conditions.