Facial Emotion Recognition Using Light Field Images with Deep Attention-Based Bidirectional LSTM
Bibliographic record
Abstract
Light field cameras are able to capture the intensity of light rays coming from multiple directions, thus representing the visual scene from multiple viewpoints. This paper exploits the rich spatio-angular information available in light field images for facial emotion recognition. In this context, a new deep network is proposed that first extracts spatial features using a VGG16 convolutional neural network. Then, a Bidirectional Long Short-Term Memory (Bi-LSTM) recurrent neural network is used to learn spatio-angular features from viewpoint feature sequences, exploring both forward and backward angular relationships. Additionally, an attention mechanism allows our model to selectively focus on the most important spatio-angular features, thus enabling a more effective learning outcome. Finally, a fusion scheme is adopted to obtain the emotion recognition classification results. Comprehensive experiments have been conducted on the IST-EURECOM Light Field Face database using two challenging evaluation protocols, showing the superiority of our method over the state-of-the-art.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".