---
license: mit
library_name: keras
tags:
  - facial-emotion-recognition
  - emotion-classification
  - cnn
  - fer2013
  - computer-vision
pipeline_tag: image-classification
---

# Facial Emotion Recognition (FER-2013 CNN)

A convolutional neural network trained on the FER-2013 dataset to classify
grayscale 48x48 face crops into 7 emotions.

- **Source code:** https://github.com/lokeshkumar80/Facial_Emotion_Recognition
- **Demo Space:** https://huggingface.co/spaces/lokeshkumar79/facial-emotion-recognition

## Model details

- **Architecture:** CNN (Conv2D + MaxPooling blocks, Dropout, Dense, softmax output)
- **Input:** grayscale image, shape `(48, 48, 1)`, pixel values normalized to `[0, 1]`
- **Output:** softmax over 7 classes
- **File:** `finalfacialemotionmodel.keras` (the recommended, verified-working model from the source repo)

## Class order (index -> label)

```
0 angry
1 disgust
2 fear
3 happy
4 neutral
5 sad
6 surprise
```

## Usage

```python
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
import numpy as np

model_path = hf_hub_download(
    repo_id="lokeshkumar79/facial-emotion-recognition",
    filename="finalfacialemotionmodel.keras",
)
model = load_model(model_path)

EMOTION_LABELS = {0: "angry", 1: "disgust", 2: "fear", 3: "happy",
                  4: "neutral", 5: "sad", 6: "surprise"}

# face: a (48, 48) grayscale numpy array, cropped to just the face
face = face.reshape(1, 48, 48, 1) / 255.0
pred = model.predict(face)
label = EMOTION_LABELS[int(np.argmax(pred))]
```

Face detection (e.g. OpenCV Haar cascade) and cropping to the face region
before resizing to 48x48 is expected as a preprocessing step — this model
only classifies emotion given an already-cropped face.

## Training data

FER-2013 (Kaggle: https://www.kaggle.com/datasets/msambare/fer2013) —
35,887 grayscale 48x48 images across 7 emotion classes
(28,709 train / 3,589 validation / 3,589 test).

## Limitations

FER-2013 is a noisy, crowd-labeled dataset with known label-quality issues
and class imbalance (`disgust` is underrepresented). Expect lower accuracy
on `disgust` and `fear`, and degraded performance on faces/lighting/angles
not well represented in the dataset.