YOLO-OSAM: Reassembly Spatial Attention Mechanisms for Facial Expression Recognition
Bibliographic record
Abstract
Facial Expression Recognition (FER) is crucial for accurately interpreting human emotions in human-computer interactions.However, FER remains challenging due to many variations, such as facial expressions, head poses, and illumination.Spatial attention mechanisms in Convolutional Neural Networks (CNNs) help address these challenges by enhancing feature extraction, directing focus to crucial facial regions while suppressing irrelevant information.However, traditional spatial attention modules, which apply average and max pooling followed by a convolutional layer, may have limited capacity to capture complex spatial dependencies, leading to suboptimal feature representation in FER tasks.YOLOv5 was selected from among various YOLO series because of its ability to deliver high accuracy object detection and classification, its lightweight architecture, and overall efficiency to overcome these limitations, we propose YOLO-OSAM, an enhanced version of YOLOv5 designed to refine feature learning for FER.Our approach introduces (1) a fusion layer that integrates attention mechanisms to ensure robust feature extraction across varying facial expressions and (2) an enhance spatial attention mechanism incorporated into the YOLOv5 architecture to capture fine-grained facial details.In this paper, the proposed attention mechanism module separately applies max and average pooling to generate feature maps, which are refined through three convolutional layers with batch normalization and Leaky ReLU activation.These processed maps are then concatenated and further optimized using additional convolutional blocks and SoftMax activation, with residual connections enhancing feature representation.Finally, we integrate this enhanced attention mechanism into the YOLOv5 neck, improving feature extraction and refinement.Experimental results demonstrate that YOLO-OSAM achieves 79.7%, 41.7%, and 98.1% accuracy on the RAF-DB (basic), RAF-DB (compound), and CK+ datasets, respectively-outperforming the original YOLOv5 by 1.3%, 0.8%, and 1.6%.Additionally, YOLO-OSAM surpasses baseline models such as VGG16, YOLOv3, and YOLOv5, highlighting its effectiveness in enhancing FER through improved spatial attention and feature extraction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".