Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
INTRODUCTION
FACE RECOGNITION
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
COMPONENTS OF FACE RECOGNITION SYSTEMS
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
PERFORMANCE
SOFTWARE
ADVANTAGES AND DISADVANTAGES
APPLICATIONS
CONCLUSION
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
Face recognition system plays an important role when its comes to security, In this slide using of neural networking system for face recognition system has demonstrated.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
Presented by Mr. Dinesh KS
Software Developer, Livares Technologies
Introduction
Object detection is a computer technology related to computer vision and image processing that
deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or
cars) in digital images and videos.
Face detection is a computer technology being used in a variety of applications that identifies
human faces in digital images.
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Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
Presented by Mr. Dinesh KS
Software Developer, Livares Technologies
Introduction
Object detection is a computer technology related to computer vision and image processing that
deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or
cars) in digital images and videos.
Face detection is a computer technology being used in a variety of applications that identifies
human faces in digital images.
Face Recognition System for Door UnlockingHassan Tariq
This is age of Modern Technology and it's becoming necessity
for everyone. Our project is on one of the most basic
daily life security system. As there was a time, when you
had to open the door by yourself or u needed a key of
some sort or a person for guarding some room.
our project changes that view, as we have automated
that old method. It's user friendly and no human interaction
is needed.Door unlocking to provide essential security to our homes, bank lockers , server rooms , private chambers and offices etc.
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Facial recognition (or face recognition) is a type of bio-metric application that can identify a specific individual in a digital image by analysing and comparing patterns.
Face recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If we look at the mirror, we can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features.
This application take picture of your face and after storing it.
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1. Submitted to:
Mr. Amit Doegar
Assistant Professor
NITTTR Chandigarh
Face Detection & Recognition
Submitted by:
Pankaj Thakur
ME CSE (Modular)
Roll No. 171408
3. Motivation
3
Face is our primary focus of interaction with society, face
communicates identity, emotion, race and age. It is also
quite useful for judging gender, size and perhaps some
of the characteristics of the person.
4. 4
Applications [8], [12], [13], [14]
Area Applications
Information Security Access Security (OS, Database)
Data Privacy (e.g. medical records)
User authentications (trading, on-line banking)
Access Management Secure Access Authentications (restricted facilities)
Permission based system
Access log or Audit Trails
Biometrics Personal identification (national IDs, passport, voter
registration, driver licenses)
Automated identity verification (boarder control)
Law Enforcement Video Surveillance,
Suspect identification
Suspect tracking ( investigation )
Simulated aging
Forensic Reconstruction of faces from remains,
Tracing missing children.
Personal Security Home video surveillance system
Expression interpretation (driver monitoring system)
Entertainment Home video game systems
Photo camera applications
5. 5
Introduction
Face
Detection & Recognition
by
Humans
• Human brain is trained for
face detection and recognition.
Face detection and recognition
is an easy task for humans [1].
•Experimentally it has been
found that even one to three
day old babies are able to
distinguish between known
faces [2].
So how hard could it be for a computer ?
6. 6
Face Detection
• Identifies human faces in digital images.
• Identifies the pixels which represent the face in a given image.
• Also referred to as the pre-processing phase.
8. 8
Face Recognition
“Face Recognition is the task
of identifying an already
detected face as a KNOWN
or UNKNOWN face, and in
more advanced cases,
TELLING EXACTLY
WHO’S IT IS ! “ [8]
Face recognition problem statement:
Given still or video images of a scene, identify or verify one or more persons in the scene
using a stored database of faces.
9. 9
Features [9]
1. Distance between the eyes
2. Width of the nose
3. Depth of the eye socket
4. Cheekbones
5. Jaw line
6. Chin
Face
Detection
Features
Extraction
Face
Recognition
How it works?
Figure: [8]
10. 10
Face Detection using Haar Cascades [3], [10]
• Devised by Paul Viola and Michael Jones in 2001.
• Robust and very quick.
• 15 times quicker than any technique at the time of release.
• Could be operated in real-time.
• (95% accuracy at around 17 fps.)
• Feature extraction and feature evaluation.
(Rectangular features are used)
• With a new image representation their calculation is very fast.
• Classifier training and feature selection using a method called
AdaBoost. (A long and exhaustive training process)
• A degenerate decision tree of classifiers is formed.
11. Features [3], [10]
11
Edge Feature
Line Feature
Four basic types:
• They are easy to calculate.
• The white areas are subtracted from the black ones.
13. 13
Challenges in Haar Cascades [10]
• Variations in pose Head positions, frontal view, profile
view and head tilt, facial expressions.
• Illumination Changes Light direction and intensity changes,
cluttered background, low quality
images.
• Camera Parameters Resolution, color balance etc.
• Occlusion Glasses, facial hair and makeup.
14. 14
Advantages & Disadvantages [11]
Advantages:
• High detection accuracy.
• Low false positive rate.
Disadvantages:
• Computationally complex and slow.
• Longer training time.
• Less accurate on black faces.
• Limitation in difficult lightening conditions.
• Less robust to occlusion/obstacle.
15. 15
OpenCV
Open Source Computer Vision [1]
Features:
• a library of programming functions mainly aimed at
real-time computer vision.
• Originally developed by Intel.
• The library is cross-platform free for use under the
open- source BSD license.
• OpenCV supports the deep learning frameworks
TensorFlow, Torch/PyTorch and Caffe.
• Applications are object, face and gesture recognition,
lip reading, Human- Computer Interaction (HCI),
Motion Tracking, Motion understanding, and Mobile
Robotics.
16. 16
Raspberry Pi 3 Model B+
Features:[4]
•Broadcom BCM2837B0, Cortex-A53
(ARMv8) 64-bit SoC @ 1.4GHz
•1GB LPDDR2 SDRAM
• 2.4GHz and 5GHz IEEE 802.11.b/g/n/ac
wireless LAN, Bluetooth 4.2, BLE
• Gigabit Ethernet over USB 2.0
(maximum throughput 300 Mbps)
• Extended 40-pin GPIO header
• Full-size HDMI
• 4 USB 2.0 ports
• CSI camera port for connecting a
Raspberry Pi camera
• DSI display port for connecting a
Raspberry Pi touchscreen display
• 4-pole stereo output and composite
video port
• Micro SD port for loading your operating
system and storing data
• 5V/2.5A DC power input
• Power-over-Ethernet (PoE) support
(requires separate PoE HAT)
17. 17
Live Demo
on
Face Detection & Recognition
using
Python + OpenCV [5], [6], [7]
(Laptop/Desktop/Raspberry Pi Model 3 B+)
18. 18
Face Detection & Recognition has been
successfully implemented using the
technique of Haar Cascade both on
laptop/desktop and on Raspberry Pi Model 3
B+ and has been found to be a robust
technique that can be applied in various
applications
Conclusion
19. 19
References[1] https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html#tu06
[Accessed: 22-June-2019]
[2] Chiara Turati, Viola Macchi Cassia, F. S., and Leo, I. Newborns face recognition:
Role of inner and outer facial features. Child Development 77, 2 (2006), 297– 311.
[3] https://docs.opencv.org/3.3.0/d7/d8b/tutorial_py_face_detection.html
[Accessed: 22-June-2019]
[4] https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/
[Accessed: 22-June-2019]
[5] https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724
[Accessed: 22-June-2019]
[6] https://github.com/sriram251/-face_recognition
[Accessed: 22-June-2019]
[7] https://github.com/opencv/opencv
[Accessed: 22-June-2019]
[8] https://www.slideshare.net/vaishalibansalbhati/face-recognition-
vaishali?qid=3a4a0ba2-493d-470c-8156-ad157f2bec94&v=&b=&from_search=8
[Accessed: 22-June-2019]
[9] https://www.slideshare.net/awesomearjun10/face-detection-attendance-system-by-
arjun-sharma?qid=b252b129-b4e7-458a-ab74-7d8ab1e0dc3f&v=&b=&from_search=1
[Accessed: 22-June-2019]
[10] https://www.slideshare.net/AbhiroopGhatak/automated-face-detection-
system?qid=8803eb10-e5a6-4e27-afc0-9eab148ec171&v=&b=&from_search=1
[Accessed: 22-June-2019]