---
title: FaceAuthenticator
emoji: 🔥
colorFrom: red
colorTo: indigo
sdk: gradio
sdk_version: 4.29.0
app_file: app.py
pinned: false
license: mit
---
# 🤖 Model Card for Model ID: FaceAuthenticator.keras

## 📋 Model Details

### 📝 Model Description

The FaceAuthenticator.keras model is a deep learning model designed for face authentication tasks. It utilizes a VGG16 convolutional neural network (CNN) architecture to extract features from facial images and make predictions about whether the face belongs to an authorized individual. This model is typically used in applications such as face recognition systems for security or access control.

- **Developed by:** Prathmesh Patil 
- **Model type:** Convolutional Neural Network (CNN)

### 🛠️ Uses

#### 👍 Direct Use

🔍 Image Authentication: Ensures the authenticity of images, crucial in domains like forensics and journalism.

🚫 Fake Image Detection: Fights misinformation by automatically identifying and flagging manipulated or synthetic images.

🛡️ Content Moderation: Supports social media platforms and online communities by removing fake or misleading images.

🔎 Digital Forensics: Assists in investigations by verifying the authenticity of images used as evidence.

🔒 Security and Privacy: Enhances security systems by detecting forged or spoofed images for authentication purposes.

🌐 Overall Impact: Maintains the integrity and trustworthiness of visual content across various applications and domains.

#### 🔧 Downstream Use

This model can be fine-tuned for specific face authentication tasks or integrated into larger systems for access control and security applications. 🛡️

#### ❌ Out-of-Scope Use

The model may not work well for faces that significantly differ from those in the training data. It is not suitable for tasks outside of face authentication. 🚫

### ⚠️ Bias, Risks, and Limitations

The model's performance may be affected by biases present in the training data, such as underrepresentation of certain demographics. Additionally, it may struggle with low-quality images or faces occluded by accessories like glasses or hats. ⚠️

#### 💡 Recommendations

Users should be aware that the model was trained with a specific dataset and may not generalize well to all populations. Consider additional authentication methods or human verification for critical decisions based on its predictions. 🤔

## 🚀 How to Get Started with the Model

Use the code below to get started with the model:

- code.py


## 🧠 Training Details

### 📊 Training Data

The model has been trained on a dataset containing facial images labeled for authentication purposes.
rvf10k
├── dataset-metadata.json
├── train
│   ├── fake
│   └── real
├── train.csv
├── valid
│   ├── fake
│   └── real
└── valid.csv

#### 📊 Training Hyperparameters

- Training regime: VGG16 with 10 epochs
- Accuracy: Approximately 82%


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference