Android App Locker Using Face Recognition — Contactless App Security

1. Introduction

The Android App Locker Using Face Recognition boosts mobile privacy by enforcing facial biometrics before opening selected apps. Built with OpenCV and deep learning (CNN), it verifies a live face in real time to unlock apps—no PINs or patterns needed. A friend can watch you type, but they can’t borrow your face. The system integrates with Android via Kivy/Flask or an API bridge to deliver secure, user-friendly protection.

2. Existing System vs Proposed System

3. Working

1. Face Data Enrolment: Capture multiple face images in varied lighting.
2. Feature Extraction: Generate embeddings (OpenCV/CNN).
3. Model Training: Train classifier for the authorized user.
4. App Lock Trigger: On opening a protected app, camera activates.
5. Authentication: Compare live face with stored embeddings.
6. Access Control: Match → unlock; mismatch → deny & log.

4. Technology Stack

• Language: Python
• Libraries: OpenCV, face_recognition, dlib, NumPy, Kivy/Flask
• Model: CNN-based recognition or LBPH baseline
• Backend: Firebase/SQLite for users, apps, logs
• Tools: Android Studio, ADB/API for bridge
• Security: AES for data encryption; optional liveness

5. Modules

6. Advantages

• Contactless biometric security for apps
• No password sharing risks
• Fast, convenient authentication
• Works with any Android app via bridge
• Optional liveness against photo/video spoofing

7. Applications

• Mobile privacy for Android devices
• Enterprise app access control
• Personal protection for chats, finance, gallery
• Academic/research in biometrics

Python + Android Bridge Sketch (OpenCV/CNN + Kivy/Flask)

# 1) Enrollment
imgs = capture_frames(device_camera, n=40, vary_lighting=True)
faces = [align_face(detector(img)) for img in imgs]
embs  = [embed(face) for face in faces]  # CNN / face_recognition encodings
db.save(user_id, average(embs))

# 2) App launch hook (Android intent/overlay/bridge)
def on_app_open(package):
    frame = capture_frame()
    face  = align_face(detector(frame))
    emb   = embed(face)
    score = cosine_sim(emb, db[user_id].embedding)
    if score > THRESH and liveness_ok(frame):
        allow_launch(package)
    else:
        deny_and_log(package); prompt_fallback_pin()

# 3) Liveness (optional)
def liveness_ok(frame_seq=None):
    # blink/micro-motion/texture checks
    return (blink_detected or texture_nonflat) and not printed_spoof
              

What You Get