A New Approach of Facial Expression Recognition for Ambient Assisted Living
Why this work is in the frame
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Bibliographic record
Abstract
Ambient Assisted Living and Ambient Intelligence have seen their impact greatly grows, especially these last decades. It is mainly due to the increase of the ageing population and people with cognitive diseases. Several technologies were developed to make the use of assistive technology more acceptable and comfortable for the elderly in order to reduce or even replace the human assistance. However, there are many challenges and issues, especially in the interaction between the elderly and assistive systems. To make the system interact as human beings, emotions were used. In this paper, we present a new approach to recognize emotions based on facial expressions represented by images. It is based on a new method for feature selection based on distances. We also suggest the use of the well-known K-Nearest Neighbor classifier with optimized parameters. This approach is found effective when tested using two different datasets of images.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it