---
library_name: sklearn
tags:
- tabular-classification
- emotion-recognition
- mediapipe
- face-blendshapes
model_format: pickle
model_file: model.pkl
language:
- en
metrics:
- accuracy: 0.5136
widget:
- structuredData:
    eyeBlinkLeft: 0.12
    eyeBlinkRight: 0.15
    browInnerUp: 0.45
    mouthSmileLeft: 0.85
    mouthSmileRight: 0.82
license: apache-2.0
---

# MediaPipe Face Emotion Classifier

This model classifies facial expressions into **7 categories** using **MediaPipe Face Landmarker Blendshapes** and a **Random Forest Classifier**.

## Performance Summary
This model was trained on the **FER-2013** dataset. Given the difficulty and noise of $48 \times 48$ grayscale images, the model achieves a strong baseline by utilizing high-level muscle movement features (Blendshapes).

- **Overall Accuracy:** 51.36%
- **Baseline (Random Guess):** 14.28%
- **Human Baseline (FER-2013):** ~65-68%

### Detailed Metrics
| Emotion | Precision | Recall | F1-Score |
| :--- | :--- | :--- | :--- |
| **Happy** | 0.66 | **0.80** | 0.72 |
| **Surprise** | 0.68 | **0.71** | 0.70 |
| **Disgust** | 0.71 | 0.50 | 0.59 |
| **Neutral** | 0.41 | 0.48 | 0.44 |
| **Angry** | 0.40 | 0.41 | 0.41 |
| **Fear** | 0.38 | 0.27 | 0.32 |
| **Sad** | 0.32 | 0.30 | 0.31 |

[Image of a Confusion Matrix for facial emotion recognition]

## Model Architecture
### 1. Feature Extraction
Instead of raw pixel data, we use the **52 Blendshape scores** provided by MediaPipe. These scores represent specific facial muscle activations (e.g., `browInnerUp`, `mouthSmileLeft`). This makes the model robust to different lighting conditions and head poses.

### 2. Classifier
- **Algorithm:** Random Forest Classifier
- **Estimators:** 200
- **Optimization:** Trained with `class_weight='balanced'` to ensure fair representation of rare emotions like *Disgust*.

## How to use
You can load this model using the `skops` library:

```python
import skops.io as sio

# Load the model
model = sio.load("model.skops", trusted=True)

# Expects an array of 52 blendshape scores
# prediction = model.predict([blendshape_list])