Multimodal Human Facial Emotion Recognition Using DenseNet-161 and Image Feature Stabilization Algorithm
Bibliographic record
Abstract
Human Facial Emotion Recognition (FER) is the technology to predict listener’s emotion of static images and videos to uncover data on one's enthusiastic states like happy, sad, frustration, anxiety, surprise, hate and neutral states. It's a part of the affective computing technology, which may be a collaborative area of research on listener’s emotions. The ability of a computer to recognise and understand and frequently explain human emotions and emotional states which relies on computing (AI) technology. The most difficult part of the FER is to plan the various looks with the separate enthusiastic states. The standard steps of Facial Emotion Recognition are i) Face RoI identification ii) Feature Extraction and iii) Emotion Recognition. Convolutional Neural Network models are the most commonly utilised to detect listener emotions. In this paper, an Image Feature Stabilization Algorithm (IFSA) is proposed to improve the efficiency of facial emotion recognition by implementing Deep Convolutional Neural Network (DCNN) model using the Transfer Learning (TL) technique. The architecture entails employing a FER-compatible pre-trained Densenet-161 based DCNN model and then fine-tuning the model for face emotion data. Initially, the dense layer(s) is/are trained, followed by the fine-tuning of each of the pre-trained DCNN blocks, resulting in an improvement in FER accuracy, particularly for difficult front face views like partial view. Experiments when performed on the CK+ dataset by employing a 10-fold cross validation method using various pre-train models like VGG 16, VGG19, ResNet-18, 34, 50, 152, Inception-V3 and DenseNet-161 showed accuracy of 85.9%, 94.6%, 91%, 91%, 91.1%, 95.1%, 97.1% and 98.7% respectively. Thus, Facial Emotion Recognition performed using the finetuned DenseNet-161 architecture demonstrated exceptionally improved accuracy compared to other pretrained models, along with the proposed image feature stabilization algorithm. The proposed architecture using Densenet-161 showed improved accuracy of 98.78% and 97.52% in other challenging FER datasets like KDEF and JAFEE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".