When we see somebody our eyes acts as a scanner and our brain as a database and we recognize them provided his image is in your brain
When we see people's faces it activates a small region of the brain, known toneuro. When we look at any other type of object this tissue normally stays quiet.
That means when we see a stranger’s face we go blank and cant recognize ,and the simple reason for that is that we don’t have his face in our database (BRAIN)
CAN YOU IDENTIFY HIM
Every face has numerous, distinguishable landmarks , the different peaks and valleys that make up facial features.
FaceIt defines these landmarks as nodal points . Each human face has approximately 80 nodal points.
Some of these measured by the software are
Distance between the eyes
Width of the nose
Depth of the eye sock
The shape of the cheekbones
The length of the jaw line
Two problems in face recognition
In this photo we can visually see the
illumination problem ,the software
cannot recognize areas of dark illumination
In this photo we can see the person
is giving a pose which causes a
difficulty for the software
The pose problem The illumination problem
How to solve illumination problem ?
Heuristic methods including discarding the leading principal components
A photo is captured and it is normalized such that the pixels illumination gets increased and we get a clear picture for further processing .
How to solve the pose problem ?
Multiple images based methods when multiple images per person are available
Here we can take a 2d photo and then reconstruct it in 3d and then we can changing the pose illumination and expression using software .This helps us to compare the same kind of pose or
3D facial recognition
Facial detection :DEMO
Faces decompose into 4 main organs
Human skin has its own color distribution that differs from that of most of nonface objects.
Cameras also use the same technology to detect faces
Once it detects a face, the system determines the head's position, size and pose.
In real life we saw that we don’t get faces that are frontal and the software is not so intelligent that it can use those images and hence we align the expression ,pose as we need
After alignment is done, we now measure every detail of the face we want to compare
The system measures the curves of the face on a sub-millimeter (or microwave) scale and creates a template
The system translates the template into a unique code. This coding gives each template a set of numbers to represent the features on a subject's face.
This unique code is what needed when we are going to compare the faces with the faces present in the database
Verification or Identification
In verification, an image is matched to only one image in the database (1:1).
If identification is the goal, then the image is compared to all images in the database resulting in a score for each potential match (1:N).
Future Uses of Facial Recognition Systems
It will be mainly used in law enforcement agencies security
Some government agencies have also been using the systems for security and to eliminate voter fraud
The U.S. government has recently begun a program called US-VISIT (United States Visitor and Immigrant Status Indicator Technology), aimed at foreign travelers gaining entry to the United States.
Critics say it produces too many false positives
Invasion of privacy
To easy to misuse for wrong purposes
Not Everyone Loves Face Recognition
At present it is most promising for small- or medium-scale applications, such as office access control and computer log in; it still faces great technical challenges for large-scale deployments such as airport security and general surveillance
Advancements in hardware and software needed
Slow integration into society in limited environments
Very large potential market
J. Gilbert and W. Yang. A Real-Time Face Recognition System using Custom VLSI Hardware. Harvard Undergraduate Honors Thesis in Computer Science, 1993.
M. Turk and A. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience , 3(1), 1991