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---
language: en
license: apache-2.0
library_name: tensorflow
tags:
- tensorflow
- keras
- tflite
- emotion-recognition
- transformer
- lstm
- mediapipe
- computer-vision
- deep-learning
- facial-expression
- affective-computing
- sequential-data
model-index:
- name: emotion_landmark_lstm_model
results:
- task:
type: sequence-classification
dataset:
type: dataset
name: Optimized 478-Point 3D Facial Landmark Dataset
metrics:
- name: accuracy
type: float
value: 0.7289
inference: "Supports TensorFlow and TensorFlow Lite real-time inference"
---
# 🎥 Emotion Sequence Transformer (TensorFlow) — Mediapipe 478 Landmarks (Seq256)
**Version:** v1.0
**Framework:** TensorFlow 2.x
**Optimized format:** TensorFlow Lite
**Input:** 478 Mediapipe Face Mesh landmarks per frame (up to 300 frames)
**Output:** 6-class emotion prediction (`Angry`, `Disgust`, `Fear`, `Happy`, `Neutral`, `Sad`)
---
## 🧠 Model Overview
The **Emotion Sequence Transformer** is a deep learning model built using TensorFlow for recognizing **human emotions** from continuous **video clips**.
It uses **478 Mediapipe facial landmarks per frame** to capture spatiotemporal patterns of facial movements across time.
The model predicts one of six basic emotions by analyzing both facial geometry and temporal variation within sequences of up to **300 frames**.
This model is suitable for **real-time video-based emotion detection**, **affective computing**, **human-computer interaction**, and **emotion-aware AI systems**.
---
## 📊 Dataset
This model was trained on the **[Optimized 478-Point 3D Facial Landmark Dataset](https://www.kaggle.com/datasets/psewmuthu/optimized-video-facial-landmarks)** —
a dataset derived from the **Video Emotion Dataset**, optimized for emotion recognition using Mediapipe’s 3D face mesh landmarks.
Each sample in the dataset includes:
- Up to **300 frames per clip**
- **478 facial landmarks per frame**
- Corresponding **emotion label**
---
## 🧩 Model Architecture
The architecture is based on a **Transformer encoder** design that processes sequential data of facial landmarks.
**Pipeline:**
1. Input normalization using precomputed mean and std (global stats)
2. Sequence embedding via positional encodings
3. Transformer encoder blocks to capture temporal and spatial dependencies
4. Dense layers for emotion classification (6 output neurons with softmax)
**Core Components:**
- Transformer Encoder Layers (Multi-Head Self-Attention)
- Layer Normalization and Dropout
- Dense classification head
---
## 📈 Performance
| Metric | Value |
| --------------------- | ---------- |
| **Test Accuracy** | 0.7289 |
| **Test Loss** | 1.1336 |
| **Macro F1-Score** | 0.73 |
| **Weighted F1-Score** | 0.73 |
| **Max Clip Length** | 300 frames |
| **Input Shape** | (300, 478) |
### 🧾 Classification Report
| Emotion | Precision | Recall | F1-score | Support |
| -------------------- | --------- | ------ | ------------------- | ------- |
| Angry | 0.75 | 0.73 | 0.74 | 139 |
| Disgust | 0.88 | 0.70 | 0.78 | 128 |
| Fear | 0.52 | 0.60 | 0.55 | 114 |
| Happy | 0.88 | 0.97 | 0.92 | 129 |
| Neutral | 0.66 | 0.79 | 0.72 | 101 |
| Sad | 0.70 | 0.58 | 0.64 | 134 |
| **Overall Accuracy** | **0.73** | | **Macro Avg: 0.73** | 745 |
---
## 📊 Visualizations
### 🔹 Training Accuracy and Loss
### 🔹 Confusion Matrix
### 🔹 ROC Curves (Per Class)
---
## 📂 Repository Structure
```
TF-Emotion-Sequence-Transformer/
├── tf_emotion_sequence_transformer_mp478_seq256.h5
├── tf_emotion_sequence_transformer_mp478_seq256_optimized.tflite
├── tf_emotion-sequence-transformer-bilstm-usage.ipynb
├── assets/
│ ├── global_mean.npy
│ ├── global_std.npy
│ ├── label_encoder.pkl
│ └── metadata.json
└── README.md
```
### File Descriptions
| File | Description |
| --------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
| `tf_emotion_sequence_transformer_mp478_seq256.h5` | Main TensorFlow model trained on 478 landmarks (300 frames max). |
| `tf_emotion_sequence_transformer_mp478_seq256_optimized.tflite` | Optimized TensorFlow Lite version for deployment (mobile, edge). |
| `tf_emotion-sequence-transformer-bilstm-usage.ipynb` | Example notebook demonstrating how to use the model for emotion prediction from Mediapipe landmarks. |
| `assets/global_mean.npy` | Precomputed global mean for normalization. |
| `assets/global_std.npy` | Precomputed global standard deviation for normalization. |
| `assets/label_encoder.pkl` | Encoder mapping integer labels to emotion names. |
| `assets/metadata.json` | Model metadata and configuration details. |
---
## 🚀 Example Usage
### 🔸 TensorFlow (.h5) Model
```python
import numpy as np
import tensorflow as tf
import joblib
import json
# Load Model
model = tf.keras.models.load_model("tf_emotion_sequence_transformer_mp478_seq256.h5")
# Load assets
mean = np.load("assets/global_mean.npy")
std = np.load("assets/global_std.npy")
label_encoder = joblib.load("assets/label_encoder.pkl")
# Preprocess input
input_seq = np.load("example_input.npy") # shape: (300, 478)
input_seq = (input_seq - mean) / std
input_seq = np.expand_dims(input_seq, axis=0)
# Predict
pred = model.predict(input_seq)
emotion = label_encoder.inverse_transform([np.argmax(pred)])[0]
print("Predicted Emotion:", emotion)
```
---
### 🔸 TensorFlow Lite (Optimized) Model
```python
import numpy as np
import tensorflow as tf
import joblib
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="tf_emotion_sequence_transformer_mp478_seq256_optimized.tflite")
interpreter.allocate_tensors()
# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Load preprocessing assets
mean = np.load("assets/global_mean.npy")
std = np.load("assets/global_std.npy")
label_encoder = joblib.load("assets/label_encoder.pkl")
# Prepare input
input_seq = np.load("example_input.npy") # shape: (300, 478)
input_seq = (input_seq - mean) / std
input_seq = np.expand_dims(input_seq, axis=0).astype(np.float32)
# Inference
interpreter.set_tensor(input_details[0]['index'], input_seq)
interpreter.invoke()
pred = interpreter.get_tensor(output_details[0]['index'])
# Decode emotion
emotion = label_encoder.inverse_transform([np.argmax(pred)])[0]
print("Predicted Emotion:", emotion)
```
---
## 🔖 Version Information
**Version:** v1.0
**Date:** November 2025
**Author:** [P.S. Abewickrama Singhe](https://www.kaggle.com/psewmuthu)
**Framework:** TensorFlow 2.x
**Exported Models:** `.h5`, `.tflite`
**Landmarks per frame:** 478
**Max frames per clip:** 300
---
## 🏷️ Tags
`tensorflow` • `emotion-recognition` • `mediapipe` • `transformer` • `sequence-model` • `facial-landmarks` • `video-analysis` • `tflite` • `human-emotion-ai` • `affective-computing` • `computer-vision` • `deep-learning`
---
## 📚 Citation
If you use this model in your research, please cite it as:
```bibtex
@misc{pasindu_sewmuthu_abewickrama_singhe_2025,
author = { Pasindu Sewmuthu Abewickrama Singhe },
title = { EmotionFormer-BiLSTM (Revision f329517) },
year = 2025,
url = { https://huggingface.co/PSewmuthu/EmotionFormer-BiLSTM },
doi = { 10.57967/hf/6899 },
publisher = { Hugging Face }
}
```
---
## 🪪 License
This model is released under the **Apache 2.0 License** — free for academic and commercial use with attribution.
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