FACE RECOGINITION ANDFACE DETECTION

LaxmanaRao.G dept.(E.C.E)

BY:GUIDE: SINDHU Assistant professor dept.(E.C.E)

FACE RECOGIFACEFACE RECOGINITION ANDFACE DETECTIONFACE RECOGINITION ANDFACE DETECTION RECOGINITION ANDFACE DETECTIONNITION ANDFACE DETECTION

LaxmanaRt.(E.C.E)

BY:GUIDE: SINDHU Assistant professor dept.(E.C.E)

INTRODUCTION:FACE RECOGINTION:

Face Recognition is used due to its fast and accurate nature

Face recognition can be used in case of ATM card being stolen or lost.

we use biometrics method to study the face recogintion method

Image retrieval plays an important role in the re-search of multimedia information indexing and retrieval, and also the techniques.

for efficient and effective image retrieval have many significant and useful applications in data mining, internet intranet search, as well as in-formation retrieval in many unstructured, multimedia databases.

 The efficiency of image retrieval concerns the response time it takes after a query or input to find a problem which is entered. Efficiency issue also relates or depends to the size of the database, and more importantly, also relates to the indexing scheme

Face detection:

Biometrics:A biometric is a unique, measurable

characteristic of a human being .It can be used to verify or automatically recognize an individual or verify an individual’s identity. It can measure both physiological and behavioural characteristics. In Physiological biometrics we consider the parts like finger scan facial recognition iris scan retina scan hand scan

Behavioural biometrics include Voice-scan Signature-scan Keystroke-scanA “biometric system” can be integrated both in hardware and software used for the identification or verification.

Why we choose face recognition over other biometric?

1.It requires no physical inert action on behalf of the user.2. It is accurate and allows for high enrolment and verification rates.3. It does not require an expert to interpret the comparison result.

THE FACE:Now a days computers are using up this face recognition

technique. In this technique it is having two types of comparisons Identification Verification

VERIFICATION:Used to compare the given individual with who that individual says they are gives yes or no decision.

IDENTIFICATION: this is where the system compares the given individual with the data base and give the ranked list.

To follow the identification and verification process they requires four stages

capture Extraction Comparison

Match/Non

Match

Accept/Reject

CAPTURING AN IMAGE BY STANDARD VIDEO CAMERA

IT IS COLLECTION OF BRIGHT AND DARK AREAS AND REPRESENTING THE PICTURE DETAILS. IT IS SIMULTANEOUSLY GIVING THE LEVEL OF BRIGHTNESS.

picture information is a function of two variables: time and space. Therefore to transmit the optical information of picture elements simultaneously it requires infinite number of channels.Transmission is carried out element by element one at a time in a sequential manner to cover the entire image. Each element consists of different resistance as the beam moves across the target plate depending on the resistance of the photoconductive coating. The result of this the current flows which varies in magnitude as elements are scanned

•COMPONENTS OF FACE RECOGNITION SYSTEM:

• IT IS AUTOMATIC MECHANISM THAT SCANS AND CAPTURES A DIGITAL IMAGE OR AN ANALOGY IMAGE.• THERE ARE TWO MODULES: 1.DATA BASE MODULE 2. VERIFICATION MODULE

IMPLEMENTATION OF FACE RECOGNITION TECHNOLOGY:The implementation of face recognition technology includes the following four stages:o data acquisitiono input processingoface image classification and decision making

Data acquisition:The input can be taken as an image or a recorded video of a speaker.

Input processing:A pre-processing module locates the eye position and takes care of the surrounding lighting condition and also color variance. First the in the image scene must be detected.

There are about 80 nodal points on a human face. Here are few nodal points that are measured by the software Distance between the eyes Width of the nose Depth of the eye socket Cheekbones Jaw line Chin

Matching:The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representationThe heart of facial recognition system is the Local Feature Analysis (LFA) algorithm.

ADVANTAGES AND DISADVANTAGES Advantages:

1.convenience and social acceptability. 2.Face recognition is easy to use 3.Can be performed without a person even knowing. 4.Face recognition is also one of the most inexpensive biometric in

the market and its prices should continue to go down.

Disadvantage:Face recognition systems can't tell the difference between identical twins.

APPLICATIONS:

The natural use of face recognition technology is the replacement of PIN, physical tokens or both needed in automatic authorization or identification schemes.

Government Use1. Law Enforcement: Minimizing victim trauma by narrowing mugs hot searches, verifying identify for court records, and comparing school

surveillance camera images to known child molesters.Security/ Counterterrorism. Access control, comparing surveillance images to known terrorists. Immigration: Rapid progression through Customs. Commercial UseDay 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 'proxy' voting where the vote may not go as expected. Banking using ATM: The software is able to quickly verify a customer's face.

Face detection:In first application area, we can identify the people in the

image .but it is still difficult and it becomes a problem. Face detection can be done by collateral texting by accompanying a captions or headings to an image. By general appearance, it is meant that the faces appearing in the images may exhibit arbitrary variations in their pose (from frontal views to different angles of side and tilt views, orientation (head inclined to the left, right, etc., scale (from very small to very large, expression (smiling, crying, sober, etc., contrast (from very dark to very bright, as well as background (may be very complex.The face detection developed here satisfies the both requirements. So this is the disadvantage .so we are considering the topic of colour imagery

The colour imagery is simpler and easy . The detection process is accomplished in two major steps: Feature Classification and Candidate Generation.

Feature classification: In order to conduct this type pattern classification, the first

problem is to define the proper features. In this work, we use two colour features: hue and chrominance.

Hue is said to be as the basic colour as it appears to the observer in the sense that certain value of hue is roughly proportional to the wavelength of its particular colour

The chrominance, on the other hand, is the measure of the lack of whiteness in a colour

The hue feature is then used to classify between face and non-face based on a Bayesian rule

Giving a particular triple of RGB values of a colour image, there are many ways to transform them into hue and chrominance values. This method was invented by Gong and Saatchi

Table 1 Lycos Image Retrieval Example

Number of Top Retrieved Images Precision

5 0%

10 20%

20 40%

30 50%

Manual examination of those irrelevant documents that were retrieved shows that they have strong relationship to the query Bill Clinton in the text even though in the images there is no Bill Clinton at all.

:

Candidate GenerationCandidate Generation

Candidate Generation:This technique is used to obtain the real face candidates; a

morphological operation is applied.

In order to “clean out" the noise of the binary image before any clustering Then, a connected component search is conducted to collect all the “big" clusters.

Face Detection vs. Facial RecognitionFace detection is anonymous by definition. The software discerns a human form” by focusing upon distinct facial features to return a quantitative determination of gender, age bracket and ethnic origin. No attempt is made to obtain a positive identification of the person within the visible field. The digital image is not matched against a database of known individuals. It is simply a data collection device That smart marketing folks are leveraging to obtain a higher advertising “cost per thousand” fee from advertisers hoping to use the data to obtain increased sales via conversion and brand loyalty.In contrast to face detection is the science of facial recognition. This software solution concentrates on making a positive identification of the individual against a database that archives personal information. Confidence factor is a key metric to avoid improper identification.

Conclusion: We have proposed an efficient and effective method for face detection from color images. We have shown that even though this method is not a perfect solution to face detection in general (i.e. with relatively high false positives and low detection rate as compared with several dedicated face detection systems in the image understanding literature This face detection system is good enough to be applied in image retrieval in certain popular domains such as consumer photos or news photos, due to its appealing efficiency. We also have shown two application examples of this face detection