Presented By :
Navin Gupta

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Face Recognisation technology
Introduction
 The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face.
 This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication.
 Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences.
 Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes.
 Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins.
Biometrics
 A biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual’s identity.
 Biometrics can measure both physiological and behavioral characteristics.
 Physiological biometrics -based on measurements and data derived from direct measurement of a part of the human body.
 Behavioral biometrics -based on measurements and data derived from an action.
TYPES OF BIOMETRICS
 PHYSIOLOGICAL
a.Finger-scan
b. Facial Recognition
c. Iris-scan
d. Retina-scan
e. Hand-scan
 BEAVIORAL
a. Voice-scan
b. Signature-scan
c. Keystroke-scan
WHY WE CHOOSE FACE RECOGNITION TECHNOLOGY
 It requires no physical interaction on behalf of the user.
 It is accurate and allows for high enrolment and verification rates.
 It does not require an expert to interpret the comparison result.
 It can use your existing hardware infrastructure, existing camaras and image capture Devices will work with no problems
 It is the only biometric that allow you to perform passive identification in a one to.
FACE RECOGNITION
 face recognition there are two types of comparisons .
 VERIFICATION-in this the system compares the given individual with who that individual says they are and gives a yes or no decision.
 IDENTIFICATION- in this the system compares the given individual to all the Other individuals in the database and gives a ranked list of matches.
 All identification or authentication technologies operate using the following four stages:
 Capture: A physical or behavioural sample is captured by the system during Enrollment and also in identification or verification process.
 Extraction: unique data is extracted from the sample and a template is created.
 Comparison: the template is then compared with a new sample.
 Match/non match: the system decides if the features extracted from the new Samples are a match or a non match.
CAPTURING OF IMAGE BY STANDARD VIDEO CAMERAS
 The image is optical in characteristics and may be thought of as a collection of a large number of bright and dark areas representing the picture details.
 At an instant there will be large number of picture details existing simultaneously each representing the level of brightness of the scene to be reproduced.
 Therefore it would require infinite number of channels to transmit optical information corresponding to picture elements simultaneously.
 There is practical difficulty in transmitting all information simultaneously so we use a method called scanning.
 the conversion of optical information to electrical form and its transmission is carried out element by element one at a time in a sequential manner to cover the entire image.
WORKING OF VEDIO CAMERA
 A TV camera converts optical information into electrical information, the amplitude of which varies in accordance with variation of brightness.
 An optical image of the scene to be transmitted is focused by lense assembly on the rectangular glass plate of the camera tube.
 The inner side of this has a transparent coating on which is laid a very thin layer of photoconductive material. The photolayer has very high resistance when no light is falling on it but decreases depending on the intensity of light falling on it.
 An electron beam is formed by an electron gun in the TV camera tube.
 This beam is used to pick up the picture information now avilable on the target plate of varying resistace at each point.
 The electron beam is deflected by a pair of deflecting coils mounted on the glass envelope and kept mutually perpendicular to each other to achive scanning of the entire target area.
 The deflecting coils are fed seperately from two sweep oscillators, each operating at different frequencies.
 The magnetic deflection caused by current in one coil gives horizontal motion to the beam from left to right at a uniform rate and brings it back to the left side to commence the trace of the next line.
 The other coil is used to deflect the beam from top to bottom.
 As the beam moves from element to element it encounters different resistance across the target plate depending on the resistance of the photoconductive coating.
 The result is flow of current which varies in magnitude as elements are scanned.
 The current passes through the load resistance Rl connected to conductive coating on one side of the DC supply source on the other.
 Depending on the magnitude of current a varying voltage appears across the resistance Rl and this corresponds to the optical information of the picture
PERFORMANCE
 False acceptance rate (FAR) -The probability that a system will incorrectly identify an individual or will fail to reject an imposter. It is also called as type 2 error rate
FAR= NFA/NIIA
Where
NFA= number of false acceptance
NIIA= number of imposter identification attempts
 False rejection rates (FRR) -The probability that a system will fail to identify an enrollee. It is also called type 1 error rate.
 FRR= NFR/NEIA
where
 NFR= number of false rejection rates
 NEIA= number of enrollee identification attempt
 Response time: The time period required by a biometric system to return a decision on identification of a sample.
 decision Threshold: The acceptance or rejection of a data is dependent on the match score falling above or below the threshold. The threshold is adjustable so that the system can be made more or less strict; depending on the requirements of any given application.
 Enrollment time: The time period a person must spend to have his/her facial reference templatesuccessfully created.
IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY
The implementation of face recognition technology includes the following four stages:
 Data acquisition
 Input processing
 Face image classification
 decision making
Data acquisition
 The input can be recorded video of the speaker or a still image. A sample of 1 sec duration consists of a 25 frame video sequence.
 More than one camera can be used to produce a 3D representation of the face and to protect against the usage of photographs to gain unauthorized access.
Input processing
 A pre-processing module locates the eye position and takes care of the surrounding lighting condition and colour variance.
 First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and Normalization process may be required to bring the dimensions of the live facial sample in alignment with the one on the template.
Face image classification
 Some facial recognition approaches use the whole face while others concentrate on facial components and/ or regions (such as lips, eyes etc).
 The appearance of the face can change considerably during speech and due to facial expressions. In particular the mouth is subjected to fundamental changes but is also very important source for discriminating faces.
 So an approach to person’s recognition is developed based on patio- temporal modeling of features extracted from talking face. Models are trained specific to a person’s speech articulate and the way that the person speaks.
 Person identification is performed by tracking mouth movements of the talking face and by estimating the likelyhood of each model of having generated the observed sequence of features.
 The model with the highest likelyhood is chosen as the recognized person. Synergetic computer are used to classify optical and audio features, respectively.
 A synergetic computer is a set of algorithm that simulate synergetic phenomena. In training phase the BIOID creates a prototype called faceprint for each person.
 A newly recorded pattern is preprocessed and compared with each faceprint stored in the database. As comparisons are made, the system assigns a value to the comparison using a scale of one to ten. If a score is above a predetermined threshold, a match is declared. .
 Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes. The face recognition system locates the head and finally the eyes of the individual.
 A matrix is then developed based on the characteristics of the Individual’s face. The method of defining the matrix varies according to the algorithm
 This matrix is then compared to matrices that are in a database and a similarity score is generated for each comparison.
 Artificial intelligence is used to simulate human interpretation of faces. In order to increase the accuracy and adaptability, some kind of machine learning has to be implemented.
METHOD OF CAPTURING
VIDEO IMAGING
Video imaging is more common as standard video cameras can be used. The precise position and the angle of the head and the surrounding lighting conditions may affect the system performance. The complete facial image is usually captured and a number of points on the face can then be mapped, position of the eyes, mouth and the nostrils as a example.
More advanced technologies make 3-D map of the face which multiplies the possible measurements that can be made.
THERMAL IMAGING
Thermal imaging has better accuracy as it uses facial temperature variations caused by vein structure as the distinguishing traits. As the heat pattern is emitted from the face itself without source of external radiation these systems can capture images despite the lighting condition, even in the dark.
The drawback is high cost. They are more expensive than standard video cameras.
HOW FACE RECOGNITION SYSTEMS WORK
 Facial recognition software is based on the ability to first recognize faces, which is a technological feat in itself. If you look at the mirror, you can see that your face has certain distinguishable landmarks. These are the peaks and valleys that make up the different facial features. Visionics defines these landmarks as nodal points. There are about 80 nodal points on a human face. Here are few nodal points that are measured by the software.
1. distance between the eyes
2. width of the nose
3. depth of the eye socket
4. cheekbones
5. jaw line
6. chin
THE SOFTWARE
 Detection-when the system is attached to a video surveilance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. The system switches to a high-resolution search only after a head-like shape is detected.
 Alignment-Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it.
 Normalization-The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process.
 Representation-The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
 Matching- The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation.
 The heart of the FaceIt facial recognition system is the Local Feature Analysis (LFA) algorithm.
 This is the mathematical technique the system uses to encode faces.
 The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database.
 Each faceprint is stored as an 84-byte file.
ADVANTAGES
 There are many benefits to face recognition systems such as its convinence and Social acceptability.all you need is your picturetaken for it to work.
 Face recognition is easy to use and in many cases it can be performed without a Person even knowing.
 Face recognition is also one of the most inexpensive biometric in the market and Its price should continue to go down.
DISADVANTAGES
 Face recognition systems can’t tell the difference between identical twins
APPLICATIONS
 Security/Counterterrorism. Access control, comparing surveillance images to Know terrorist.
 Day Care: Verify identity of individuals picking up the children.
 Residential Security: Alert homeowners of approaching personnel
 Voter verification: Where eligible politicians are required to verify their identity during a voting process this is intended to stop voting where the vote may not go as expected.
 Banking using ATM: The software is able to quickly verify a customer’s face.