Biometric Authentication using Face/Voice Recognition - Technical & Engineering Guide
1. Introduction
1.1 Purpose
This guide outlines the implementation of a biometric authentication system that uses face and voice recognition to ensure secure access. The system combines advanced machine learning models for reliable authentication with minimal user effort.
1.2 Scope
The project focuses on developing a multimodal biometric authentication system that can be integrated into web and mobile applications. It targets scenarios requiring high-security access control, such as financial services, corporate systems, and personal devices.
1.3 Definitions & Acronyms
|
Acronym |
Definition |
|
ML |
Machine Learning |
|
AI |
Artificial Intelligence |
|
CNN |
Convolutional Neural Network |
|
MFCC |
Mel-frequency Cepstral Coefficients |
|
FAR |
False Acceptance Rate |
|
FRR |
False Rejection Rate |
|
API |
Application Programming Interface |
2. Architecture Overview
The biometric authentication system involves the following components:
- **Face Recognition Module**: Uses CNNs to analyze facial features.
- **Voice Recognition Module**: Employs MFCCs and RNNs to process and
authenticate voice patterns.
- **Data Store**: Secure storage for biometric templates and user metadata.
- **Backend Services**: API endpoints for enrollment, authentication, and
template management.
- **Client Interfaces**: Web and mobile apps for capturing biometric data.
3. Biometric Data Processing
3.1 Face Recognition Workflow
1. Capture the user's face image using a camera.
2. Preprocess the image: cropping, resizing, and normalization.
3. Extract facial embeddings using a pre-trained CNN model (e.g., FaceNet or
Dlib).
4. Compare embeddings against stored templates using cosine similarity or
Euclidean distance.
3.2 Voice Recognition Workflow
1. Record voice input using a microphone.
2. Extract MFCC features from the audio signal.
3. Feed features into a pre-trained RNN or LSTM model.
4. Authenticate by matching the extracted voice pattern with stored templates.
4. Security Considerations
1. Encrypt biometric templates using strong cryptographic
methods.
2. Use liveness detection to prevent spoofing attacks.
3. Limit retries and implement lockout mechanisms to prevent brute-force
attempts.
4. Ensure compliance with privacy laws (e.g., GDPR, CCPA) by anonymizing data
and limiting storage.
5. Implementation Plan
1. Use TensorFlow or PyTorch for model training and
inference.
2. Integrate OpenCV for face detection and audio libraries like LibROSA for
voice processing.
3. Use Flask or FastAPI to develop backend services.
4. Deploy the system using Docker and Kubernetes for scalability.
6. Testing and Validation
1. Measure model accuracy using FAR and FRR metrics.
2. Test against datasets like LFW (Labeled Faces in the Wild) for face and
VoxCeleb for voice.
3. Conduct penetration testing to identify vulnerabilities.
4. Perform user acceptance testing to ensure a seamless experience.