Eliminate manual registers and proxy attendance. A contactless, secure, and real-time facial recognition system designed for the modern campus.
Recognition Accuracy
Processing Speed
GDPR Compliant
Manual roll calls waste valuable lecture time. Proxy attendance undermines academic integrity. Physical logbooks are prone to damage and loss.
FaceAttend.AI utilizes advanced computer vision to identify students instantly as they enter. Data is logged automatically, reports are generated instantly, and proxies are eliminated.
Engineered for reliability in real-world conditions.
Uses LBPH (Local Binary Patterns Histograms) for robust face recognition, even in varying lighting conditions.
Instant identification. Students simply walk past the camera—no pausing required.
Liveness detection algorithms prevent fraud using photos or video playback.
Automatically generates daily CSV/Excel reports for faculty review at the end of sessions.
Tested with classes of 100+ students. Database scales easily without performance loss.
Data stays on your machine. No cloud dependency means higher privacy and zero latency.
From registration to reporting, the process is seamless.
Admin captures student faces via webcam. 100+ samples are taken to build a robust dataset.
The system processes the images, extracting unique facial landmarks to create a biometric profile.
During class, the camera scans for known faces. When a match is found (>85% confidence), attendance is marked.
At session end, a date-stamped CSV file is saved automatically for administrative records.
Handle attendance for large lecture halls efficiently.
Track employee entry/exit for payroll automated logs.
Secure access control for exclusive workshops or seminars.
From open-source research to enterprise-grade campus deployment.
Access the core technology for educational and research purposes.
Advanced features for active deployments in coaching centers & labs.
Full-scale implementation for universities and large organizations.
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AI & Computer Vision Engineer
Passionate about leveraging artificial intelligence to solve real-world logistical problems. This project represents 3 months of research into biometric security and edge computing.
View GitHub Profiledef recognize_face(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
for (x, y, w, h) in faces:
id, confidence = recognizer.predict(gray[y:y+h, x:x+w])
if confidence < 100:
mark_attendance(id)
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