ARXIV:2603.12221 · FACIAL EMOTION RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

arXiv

A dual-modal model for accurate facial emotional expression recognition from videos.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A dual-modal model for accurate facial emotional expression recognition from videos.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A dual-modal model for accurate facial emotional expression recognition from videos. This task is challenging due to inaccurate face localization, large pose and scale variations, motion blur, temporal instability, and other confounding factors across…

METHOD

Full abstract

This paper addresses the expression (EXPR) recognition challenge in the 10th Affective Behavior Analysis in-the-Wild (ABAW) workshop and competition, which requires frame-level classification of eight facial emotional expressions from unconstrained videos. This task is challenging due to inaccurate face localization, large pose and scale variations, motion blur, temporal instability, and other confounding factors across adjacent frames. We propose a two-stage dual-modal (audio-visual) model to address these difficulties. Stage I focuses on robust visual feature extraction with a pretrained DINOv2-based encoder. Specifically, DINOv2 ViT-L/14 is used as the backbone, a padding-aware augmentation (PadAug) strategy is employed for image padding and data preprocessing from raw videos, and a mixture-of-experts (MoE) training head is introduced to enhance classifier diversity. Stage II addresses modality fusion and temporal consistency. For the visual modality, faces are re-cropped from raw videos at multiple scales, and the extracted visual features are averaged to form a robust frame-level representation. Concurrently, frame-aligned Wav2Vec 2.0 audio features are derived from short audio windows to provide complementary acoustic cues. These dual-modal features are integrated via a lightweight gated fusion module, followed by inference-time temporal smoothing. Experiments on the ABAW dataset demonstrate the effectiveness of the proposed method. The two-stage model achieves a Macro-F1 score of 0.5368 on the official validation set and 0.5122 +/- 0.0277 under 5-fold cross-validation, outperforming the official baselines.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the ABAW dataset demonstrate the effectiveness of the proposed method.

WHY NOW

Facial Emotion Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA dual-modal model for accurate facial emotional expression recognition from videos.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A dual-modal model for accurate facial emotional expression recognition from videos.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.12221 · FACIAL EMOTION RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

arXiv

A dual-modal model for accurate facial emotional expression recognition from videos.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A dual-modal model for accurate facial emotional expression recognition from videos.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the ABAW dataset demonstrate the effectiveness of the proposed method.

WHY NOW

Facial Emotion Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA dual-modal model for accurate facial emotional expression recognition from videos.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A dual-modal model for accurate facial emotional expression recognition from videos.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.12221

A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

A dual-modal model for accurate facial emotional expression recognition from videos.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback36 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the ABAW dataset demonstrate the effectiveness of the proposed method.

WHY NOW

Facial Emotion Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A dual-modal model for accurate facial emotional expression recognition from videos. This task is challenging due to inaccurate face localization, large pose and scale variations, motion blur, temporal instability, and other confounding factors across adjacent frames.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
This paper addresses the expression (EXPR) recognition challenge in the 10th Affective Behavior Analysis in-the-Wild (ABAW) workshop and competition, which requires frame-level classification of eight facial emotional expressions from unconstrained videos. This task is challenging due to inaccurate face localization, large pose and scale variations, motion blur, temporal instability, and other confounding factors across adjacent frames.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on the ABAW dataset demonstrate the effectiveness of the proposed method.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Facial Emotion Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Facial Emotion Recognition

Competitors

not indexed

Competitive landscape

A dual-modal model for accurate facial emotional expression recognition from videos.

Segment

Facial Emotion Recognition

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.12221 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

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PDF

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References(36)

From Emotions to Violence: Multimodal Fine-Grained Behavior Analysis at the 9th ABAW

2025D. Kollias, S. Zafeiriou et al.

Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition

2025Dimitrios Kollias, Panagiotis Tzirakis et al.

HSEmotion Team at ABAW-8 Competition: Audiovisual Ambivalence/Hesitancy, Emotional Mimicry Intensity and Facial Expression Recognition

2025A. Savchenko

AffectNet+: A Database for Enhancing Facial Expression Recognition With Soft-Labels

2024A. P. Fard, M. Hosseini et al.

7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition

2024D. Kollias, S. Zafeiriou et al.

Advanced Facial Analysis in Multi-Modal Data with Cascaded Cross-Attention based Transformer

2024Jun-Hwa Kim, Namho Kim et al.

EmotiEffNet and Temporal Convolutional Networks in Video-based Facial Expression Recognition and Action Unit Detection

2024A. Savchenko, A. Sidorova

SUN Team's Contribution to ABAW 2024 Competition: Audio-visual Valence-Arousal Estimation and Expression Recognition

2024D. Dresvyanskiy, Maxim Markitantov et al.

Exploring Facial Expression Recognition through Semi-Supervised Pre-training and Temporal Modeling

2024Jun Yu, Zhihong Wei et al.

The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

2024D. Kollias, Panagiotis Tzirakis et al.

Logo-Former: Local-Global Spatio-Temporal Transformer for Dynamic Facial Expression Recognition

2023Fuyan Ma, Bin Sun et al.

DINOv2: Learning Robust Visual Features without Supervision

2023M. Oquab, Timothée Darcet et al.

ABAW : Facial Expression Recognition in the wild

2023Darshan Gera, Badveeti Naveen et al.

ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges

2023D. Kollias, Panagiotis Tzirakis et al.

ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges

2022D. Kollias

ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges

2022D. Kollias

Former-DFER: Dynamic Facial Expression Recognition Transformer

2021Zengqun Zhao, Qingshan Liu

Analysing Affective Behavior in the second ABAW2 Competition

2021D. Kollias, I. Kotsia et al.

Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework

2021D. Kollias, S. Zafeiriou

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

2020Alexey Dosovitskiy, Lucas Beyer et al.

Showing 20 of 36 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkSolution to the 10th ABAW Expression Recognition Challenge: A Robust Multimodal Framework with Safe Cross-Attention and Modality Dropout

7.0

Prior WorkHierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild

7.0

Prior WorkAnchoring Emotions in Text: Robust Multimodal Fusion for Mimicry Intensity Estimation

7.0

Extension

Builds On ThisMultimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling

6.0

Builds On ThisTeam RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

4.0

Builds On ThisTeam LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach

4.0

Builds On ThisFoundation Model Embeddings Meet Blended Emotions: A Multimodal Fusion Approach for the BLEMORE Challenge

4.0

Builds On ThisDual Stream Independence Decoupling for True Emotion Recognition under Masked Expressions

4.0

Builds On ThisDual-Model Prediction of Affective Engagement and Vocal Attractiveness from Speaker Expressiveness in Video Learning

5.0

Commercially relevant

none indexed

Conflicting

Competing ApproachHSEmotion Team at ABAW-10 Competition: Facial Expression Recognition, Valence-Arousal Estimation, Action Unit Detection and Fine-Grained Violence Classification

6.0

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Agent drawer

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Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.12221
Route: /paper/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition
Active tab: read
Artifact: a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition

Available agents

Read extractor
Build planner
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Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition/paper-pack
REST build passport API/api/v1/paper/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

Canonical ID a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition | Route /paper/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition

MCP example

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source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

/buildability/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition

Watchwatch

Subject: A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition

Paper ref

a-two-stage-dual-modality-model-for-facial-emotional-expression-recognition

arXiv id

2603.12221

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

7f83d996299acfca6681cdfd85d6f3b459fc8578f305f65188422be48ab1a3c8

Canonical opportunity-kernel lineage hash.

Signature state

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No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

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