The document presents a face recognition security system built on a Raspberry Pi platform that utilizes a camera and various classifiers for authentication. It highlights the software and hardware components, including algorithms for face detection and recognition, and emphasizes its ability to operate without manual observation. The system is designed for efficient operation with a focus on illumination and frontal face capture.

1. RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
The invention is all about a security system and implementation method. The system
comprises of pi-camera, face detection classifier, trained database, programmes with
programming libraries, raspberry pi-processor module, face recognition classifier,
keyboard, mouse, motor and LCD screen.
System has advantage that it can verifies or authenticate a person through face
without using large computer or any man observations; and opens the door or keep it
locked as per the result. It comprises of a new entry configuration for newies.
Example: If person ‘X’ visits a place ‘A’ with this system and have/haven’t previously
stored the facial data in place ‘A’ database; then door will be opened/closed;
“matched/not matched” will be displayed on screen according to the result for ‘X’.
ABSTRACT
Block diagram
CONCLUSION
Face Recognition system Using Raspberry Pi
Fig 1: Security System
HARDWARE
Algorithm
Fig 6: Face Detection
Fig 8: Face Recognition
Fig 4: AT&T
database
Fig 7:PCA for Database
Face recognition system based on Raspberry Pi single-board computer is divided
in two parts -the software part and the hardware part. Software part describes the
algorithms for face detection, localization, feature extraction and recognition.
Hardware part describes how the system was built and what modules does it use.
PCA based facial detection and recognition system uses the Raspberry Pi
development platform. The software codes for both detection and recognition of
faces are written using C++ library of OpenCV resulting in reduction of background
noise and pre-processing is minimized. The system works best when the face is
sufficiently illuminated and the person is frontal w.r.t. the camera.
Fig 9: METHODOLOGY
Face recognition SOFTWARE
Fig 2: Difference Fig 5: Model B3
Database REFERENCES
• Eigenfaces for Recognition”, Turk, M. and Pentland A., (1991) Journal of
Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86.
• file://localhost/C:/Program%20Files/OpenCV/docs/index.htm
• www.raspberrypi.com.
• Sarala A. Dabhade and Mrunal S. Bewoor, Real Time Face Detection and
Recognition using Haar – Based Cascade Classifier and Principal Component
Analysis, International Journal of Computer Science and Management
Research, Aug 201 2, Vol 1 , Issue 1
Team members: Dimple Balasar(120090111055),Hetvi Naik(130090111055),Vatsal Champaneria(140093111005),Krunal
Parmar(140093111017),Khushbu Raj(140093111033)
Guided by: Dr. Mita Paunwala Group No: 80414
C. K. PITHAWALA COLLEGE OF ENGINEERING AND TECHNOLOGY
Result Analysis
Face detectedInput Image Eigen Faces
EIGEN FACE DATABASE
D >
THERSHOLD
MATCHED
D <
THERSHOLD
NOT
MATCHED
DISPLAY
“Sally”
O/P
Sent to set
for matching
comparing
Input Image Face detected
O/P
Recognition
Detection
❑ Face recognition is
actually divided into
2 parts:
1. Face detection
(Haar Cascade)
2. Face Recognition
(PCA Algorithm)
❑ The software section
is “OPENCV” c++
libraries with
“PYTHON” scripts.
❑ It have some inbuilt
functions with are
easy to execute the
algorithm.
❑ The AT&T
database is used
here. But we can
create our own
also.
❑ Here the Eigen
faces are stored.
❑ The credit-card size
processor called
“RASPBERRY
PI B3” model is
used as hardware
platform which
supports the whole
system.
Fig 3: OPENCV
FACIAL
DATABAS
E
Distance
calculation
The comparison is done on: