❗ Problem Summary:
The current emotion recognition component relies on a pre‑trained model (e.g., fer2013_mini_XCEPTION.102‑0.66.hdf5) that may have limited accuracy and only recognizes a small subset of emotions (e.g., happy, sad, angry, neutral). This can lead to inaccurate music recommendations when the model misclassifies expressions.
🔍 Observed Issues:
Model accuracy can be inconsistent under varied lighting and camera conditions.
Only a limited number of emotions are supported, which restricts recommendation relevance.
Lack of evaluation metrics or feedback mechanism to assess or improve detection quality over time.
⚡ Impact:
Medium impact on overall user experience since mis‑detected emotions can lead to irrelevant music recommendations.
Reduces reliability and perceived intelligence of the system.
🛠 Suggested Fix:
Integrate a more robust pre‑trained model (e.g., one trained on larger and more diverse datasets) to improve emotion classification accuracy.
Expand recognized emotion classes (e.g., include surprise, fear, disgust) for richer recommendations.
Add unit tests/validation scripts to measure emotion detection performance and track improvements.
Consider providing users with optional camera‑free feedback (e.g., manual mood selection) to fallback if detection fails.
❗ Problem Summary:
The current emotion recognition component relies on a pre‑trained model (e.g., fer2013_mini_XCEPTION.102‑0.66.hdf5) that may have limited accuracy and only recognizes a small subset of emotions (e.g., happy, sad, angry, neutral). This can lead to inaccurate music recommendations when the model misclassifies expressions.
🔍 Observed Issues:
Model accuracy can be inconsistent under varied lighting and camera conditions.
Only a limited number of emotions are supported, which restricts recommendation relevance.
Lack of evaluation metrics or feedback mechanism to assess or improve detection quality over time.
⚡ Impact:
Medium impact on overall user experience since mis‑detected emotions can lead to irrelevant music recommendations.
Reduces reliability and perceived intelligence of the system.
🛠 Suggested Fix:
Integrate a more robust pre‑trained model (e.g., one trained on larger and more diverse datasets) to improve emotion classification accuracy.
Expand recognized emotion classes (e.g., include surprise, fear, disgust) for richer recommendations.
Add unit tests/validation scripts to measure emotion detection performance and track improvements.
Consider providing users with optional camera‑free feedback (e.g., manual mood selection) to fallback if detection fails.