Face Spoof Detection for Biometric Logins – Real-Time Liveness Protection
Block photos, replays, and masks. This project analyses texture patterns, motion/blink cues, and depth consistency in real time using OpenCV and CNN models.
- ✓Real/Spoof decision with confidence & reasons
- ✓Blink/micro-motion tracking and reflection checks
- ✓Flask/Tkinter UI for live login gating
1. Introduction
The Face Spoof Detection for Biometric Logins is a Python-based Artificial Intelligence and Computer Vision project that enhances the security of facial authentication systems by distinguishing between real (live) faces and spoofing attempts such as photos, videos, or masks. The system ensures that only genuine users can access protected applications or devices. Using deep learning and image processing techniques, it analyses texture patterns, motion cues, and liveness indicators in real time. This project helps prevent unauthorized access in biometric systems widely used in mobile apps, banking, and attendance systems.
2. Existing System vs Proposed System
- Conventional face recognition matches features only—no liveness.
- Vulnerable to printed photos, replayed videos, and 3D masks.
- No intelligent detection of real-time motion/depth cues.
- Liveness via texture, blink, and motion tracking.
- CNN models trained on real vs spoof datasets.
- Detects spoofing in high-res images or video streams.
- Secure layer on top of face recognition pipelines.
- Integrates with mobile/web logins and access control.
3. Working
- Face Capture: Real-time video/image from webcam.
- Pre-processing: Grayscale & normalization for lighting/contrast.
- Feature Extraction: Texture/depth via LBP or CNN layers.
- Liveness Detection: Blink/micro-movements; reflections & edge consistency analysis.
- Classification: Trained ML/DL model outputs Real or Spoof.
- Authentication: Real → approve login; Spoof → deny & log.
4. Technology Stack
- Language: Python
- Libraries: OpenCV, NumPy, TensorFlow/Keras, dlib, scikit-learn
- Model: CNN for real vs spoof detection
- Dataset: CelebA-Spoof, CASIA-SURF, or custom dataset
- Interface: Flask/Tkinter GUI for live detection
- Hardware: Webcam or smartphone camera
5. Modules
Detects faces from camera feed.
- OpenCV/Dlib detectors
- Multi-face handling
Normalizes frames.
- Grayscale/resize
- Lighting/contrast normalization
Texture/depth features.
- LBP/CNN feature maps
- Temporal cues
CNN classification.
- Real vs Spoof label
- Confidence score
Login decision.
- Approve/Deny
- Callback hooks
Security records.
- Attempt logs
- Optional email/SMS alerts
6. Advantages
- Protects against photo/video/mask spoofing.
- Real-time and lightweight on standard devices.
- Plugs into existing biometric auth systems.
- Higher trust in AI security with liveness checks.
- Multiple cues (blink, motion, texture) for robustness.
7. Applications
- Secure biometric logins on mobile/desktop/web.
- Banking and payment verifications.
- Office/college attendance systems.
- Smart home/secure facility access control.
- R&D in identity verification.
Python Integration Sketch (Flask + OpenCV + CNN)
# Capture & detect
cap = cv2.VideoCapture(0)
while True:
ok, frame = cap.read()
face = detect_face(frame) # dlib/haar/ssd
roi = preprocess(face) # gray, resize, normalize
feats = extract_features(roi) # LBP or CNN preprocess
label, conf = model.predict(feats) # Real/Spoof
if label == "Real" and conf > THRESH: allow_login()
else: deny_and_log()
What You Get
| Item | Included | Notes |
|---|---|---|
| Python Source Code | ✅ | OpenCV + TensorFlow/Keras |
| Liveness (Blink/Motion/Texture) | ✅ | Configurable thresholds |
| CNN Model & Training Scripts | ✅ | Baseline + tuning tips |
| Flask/Tkinter UI | ✅ | Live webcam gating |
| Demo Video | ✅ | Setup & working walkthrough |
| Report & PPT | ✅ | College-format templates |
| Support | ✅ | Installation + viva Q&A (1 month) |
FAQs — Face Spoof Detection
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