Abstarct-1.docx

Face recognition using Extended local binary pattern 1. Abstract: Now a day’s keeping data or object safe is getting complicated due to digital password, pins, pattern are easily getting hacked. For the beyond the digitalized security lock everyone looking towards a better solution to get the data/object safe by using various biometric techniques like finger prints, iris scan, as well as hand geometry, the most efficient and more widely used one is the face recognition This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years . Efficiency of matching the face is always low because of the code or the data base utilized to run the code. In this project we are mainly focusing on three parts, namely face representation, feature extraction and classification. Face representation represents how to model a face and determines the successive algorithms of detection and recognition. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database. In our project, we empirically evaluate face recognition which considers both shape and texture information to represent face images based on Local Binary Patterns for person-independent face recognition. The face area is first divided into small regions from which Local Binary Patterns (LBP), histograms are extracted and concatenated into a single feature vector. With that we are using PCA, LDA for better results.

2. Introduction: Biometrics is a method of human identification by analyzing their physical and behavioral characteristics. This method is user friendly because it uses physical traits such as face recognition, recognition of palm, fingerprint and iris scanning. Face recognition received great amount of attention due to the high recognition rate since face recognition take into consideration both physical and behavioral characteristics. In this method we are using LBP, LDA, PCA for image preprocessing, feature extraction and matching process. In the preprocessing stage, the image is normalized by mean of image cropping and resize before being input to the next stage. The LBP algorithm comes into play in the feature extraction stage. Finally, the matching process will decide whether a particular face image match to the database image. The metric used in the matching process is chi-square distance.

Face Recognition: Face recognition is to use the facial characteristic for recognition and judgment. • It is one of the biometric techniques. • Two types of comparison in face recognition:  Verification: It means a 1:1 match that compares a face images against a template face images whose identity being claimed.  Identification: It means a 1: N problem that compares a query face image against all image templates in a face data base.

WHY FACE RECOGNITION? •

It does not require any physical contact with an image capturing device.

• It does not require any advanced hardware as it can be used with existing image capture devices. Face recognition consists of three main steps: 1. Face Detection. 2. Feature Extraction. 3. Classification.

3. LITERATURE SURVEY: Early face recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. The face recognition problem is made difficult by the great variability in head rotation, angle, facial expression, lighting intensity.

4. Problem statement: In real world safe guarding a something most likely the data/ object is not possible with locks and passwords. Because the technology we much more increase that helps the criminals to hack the system with different pattern, password is happened. Now a days everyone is looking for a better option of safe guarding not by pattern or password somewhat like biometric techniques in that face recognition gives more efficient.

5. Objective:

1. Increasing the efficiency in face verification (1:1) by using Extended Local Binary Pattern (ELBP) under various conditions. 2. Increasing the efficiency in face identification (1: N) by using Extended Local Binary Pattern (ELBP) under various conditions.

6. Methodology: By using Local Binary Pattern (LBP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacian faces are the optimal linear approximations to the eigen functions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LBP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisher face methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Principal Component Analysis (PCA) is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between observed variables. The jobs which PCA can do are prediction, redundancy removal, feature extraction, data compression, etc. Because PCA is a known powerful technique which can do something in the linear domain, applications having linear models are suitable, such as signal processing, image processing, system and control theory, communications, etc.

The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D face image into the compact principal components of the feature space. This is called eigenspace projection. Eigenspace is calculated by identifying the eigenvectors of the covariance matrix derived from a set of fingerprint images (vectors).

7. Applications:  Retail Stores Many retail stores are increasingly adopting face recognition to identify repeat customers and offer them special services. They are also using it to derive data and analyse the performance of their stores. Demographic details like gender and age can be used to reveal the kind of customers that frequent the store. Businesses can then optimise their products to drive more sales. The way things are headed, every store in the future will greet you by name!  Casinos Casinos are actively using facial recognition systems in order to recognize their customers. They then use it to track them, identify them on the web, and let them know how often they’re going to the casino. Recognition allows them to identify repeat customers and estimate frequency of their visit. They can then use it to help gambling addicts, reward loyal customers, identify fraudsters, and even enforce bans.  Dating sites With explosion in dating sites/apps and users showing keen interest in such services, businesses have come up with all sorts of crafty matching mechanisms to help users find their soulmates. Among such sites are the likes of findyourfacemate.com. The makers of this site believe (more like claim) that people are most attracted to those that look like them. They use neural nets to map faces to a mathematical space. People whose faces are nearby in this space are then suggested by the app as potential mates. Similarly, Doggleganger can also match you up with a DOG that looks like you! crazy!.  Government Agencies This one comes as no surprise. In countries like US and China, government institutions like FBI etc. have been using face recognition for a long time to identify criminals. The Chinese have gone full ninja with this technology, with

some reports suggesting that the government can identify criminals even before they commit the crime! They’ve been known to snoop on crowds at sporting events and other places. More power to the authorities.  Social Media Facebook and its gazillion photos of 2 Billion users uses Facial Recognition to identify everyone. There was a huge uproar and protest over users getting tagged in strangers' photos and the massacre of privacy. Following these, FB has decided to turn it off by default... for now.  Shopping Companies like MasterCard are researching about ways to enable payment verification through the face. The advantages are numerous but the primary motivation is preventing fraud and identity theft. Among other advantages is that one won't have to remember passwords or put the credit card information on the web. This will greatly reduce losses banks incur due to international credit card theft/hacking mafia. In the future, you will smile to pay for everything your wife forces you to buy!  Hotels and Restaurants Hotels and restaurants are beginning to use this service to identify customers even before they enter the door. This allows them to offer specialized services to each customer by taking into account their past preferences. Restaurants can also cater to their guests by serving them their favorite meal by preparing it as soon as they arrive.  Bars picking out underage drinkers Teenagers using fake Ids to get a drink have been a problem for quite some time. With age recognition technology it is possible to determine age from a person's face. This will prevent teenagers from getting fake ids and hopefully reduce black market activity.  Schools have started using it to take attendance Many schools have started installing this technology to automatically track attendance of pupils. This gives more time to teachers for teaching and maximizes productivity. It also prevents students from faking attendance or other mischief. It is also possible to keep a full-eye on the campus and find and track bullies.

 Access Control in Offices Many offices and government buildings are now using face recognition based authentication for seamless access control. This prevents, among other things, tail gating where people who forgot their IDs run behind their colleagues to pass the access point. It also increases security by immediately reporting potential breach attempts. Its also possible to comprehensively track an individual inside a campus/building and identify malicious visitors.

8. Advantages: 1. Security levels will be significantly improved 2. The integration process is easy and flawless 3. High accuracy allows avoiding false identification 4. Facial Recognition System is fully automated 5. Time fraud will be excluded

9. Tools Required: MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and proprietary programming language developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, C#, Java, Fortran and Python. Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing abilities. An additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems. Most MATLAB functions can accept matrices and will apply themselves to each element. For example, mod(2*J,n) will multiply every element in "J" by 2, and then reduce each element modulo "n". MATLAB does include standard "for" and "while" loops, but (as in other similar applications such as R), using the vectorized notation often produces code that is faster to execute. This code, excerpted from the function magic.m, creates a magic square M for odd values of n (MATLAB function meshgrid is used here to generate square matrices I and J containing 1:n). [J,I] = meshgrid(1:n);

A = mod(I + J - (n + 3) / 2, n); B = mod(I + 2 * J - 2, n); M = n * A + B + 1;  Structures MATLAB has structure data types. Since all variables in MATLAB are arrays, a more adequate name is "structure array", where each element of the array has the same field names. In addition, MATLAB supports dynamic field names (field look-ups by name, field manipulations, etc.). Unfortunately, MATLAB JIT does not support MATLAB structures, therefore just a simple bundling of various variables into a structure will come at a cost.  Functions When creating a MATLAB function, the name of the file should match the name of the first function in the file. Valid function names begin with an alphabetic character, and can contain letters, numbers, or underscores. Functions are often case sensitive.  Function handles MATLAB supports elements of lambda calculus by introducing function handles, function references, which are implemented either in .m files or anonymous/nested functions. Most MATLAB functions can accept matrices and will apply themselves to each element. For example, mod(2*J,n) will multiply every element in "J" by 2, and then reduce each element modulo "n". MATLAB does include standard "for" and "while" loops, but (as in other similar applications such as R), using the vectorized notation often produces code that is faster to execute. This code, excerpted from the function magic.m, creates a magic square M for odd values of n (MATLAB function meshgrid is used here to generate square matrices I and J containing 1:n). [J,I] = meshgrid(1:n); A = mod(I + J - (n + 3) / 2, n); B = mod(I + 2 * J - 2, n); M = n * A + B + 1;  Structures

MATLAB has structure data types. Since all variables in MATLAB are arrays, a more adequate name is "structure array", where each element of the array has the same field names. In addition, MATLAB supports dynamic field names(field lookups by name, field manipulations, etc.). Unfortunately, MATLAB JIT does not support MATLAB structures, therefore just a simple bundling of various variables into a structure will come at a cost.  Functions When creating a MATLAB function, the name of the file should match the name of the first function in the file. Valid function names begin with an alphabetic character, and can contain letters, numbers, or underscores. Functions are often case sensitive.  Function handles MATLAB supports elements of lambda calculus by introducing function handles, or function references, which are implemented either in .m files or anonymous/nested functions.  Graphics and graphical user interface programming MATLAB supports developing applications with graphical user interface (GUI) features. MATLAB includes GUIDE (GUI development environment) for graphically designing GUIs. It also has tightly integrated graph-plotting features. For example, the function plot can be used to produce a graph from two vectors x and y. The code: x = 0:pi/100:2*pi; y = sin(x); plot(x,y) produces the following figure of the sine function: Matlab plot sin.svg A MATLAB program can produce three-dimensional graphics using the functions surf, plot3 or mesh.

10. Plan of Work: Description of work

Duration

Data Collection Literature Survey Problem Identification Methodology Design Software Implementation Testing and Analysis of results Presentation in outside Thesis Preparation Final Submission

20/12/2018 to 12/01/2019 15/01/2019 to 28/01/2019 29/01/2019 to 02/02/2019 03/02/2019 to 08/02/2019 09/02/2019 to 09/03/2019 10/03/2019 to 12/03/2019

Remarks

15/03/2019 to 17/03/2019 18/03/2019 to 05/04/2019 06/04/2019 to 10/04/2019

REFERENCES

1. Evaluation of LBP-based Face Recognition Techniques Siti Anis Amirah Mohd Faudzi, Norashikin Yahya Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS 2. Face Recognition using Local Binary Patterns (LBP) By Md. Abdur Rahim, Md. Najmul Hossain, Tanzillah Wahid & Md. Shafiul Azam Pabna University of Science and Technology, Bangladesh 3. Review of Face Recognition Techniques Kamini Solanki Ph.D Scholar Computer Science Dept. RK University Rajkot India, Prashant Pittalia, PhD Computer Science Dept. NICM Gandhinagar India 4. A Survey of 2D Face Recognition Techniques Mejda Chihaoui , Akram Elkefi, Wajdi Bellil and Chokri Ben Amar REGIM: Research Groups on Intelligent Machines, University of Sfax, National School of Engineers (ENIS), Sfax 3038, Tunisia; 5. A Literature Survey on Face Recognition Techniques Riddhi Patel, Shruti B. Yagnik Department of Computer Engineering, Gujarat Technological University Gujarat, India Department of Information Technology, Gujarat Technological University Gujarat, India. 6. A Review Paper on Biometrics: Facial Recognition

Sakshi Goel, Akhil Kaushik, Kirtika Goel, (CSE Department, IIMT Meerut, UP Email: [email protected]) (CSE Department, SIT Meerut, UP Email: [email protected]) (CSE Department, SIT Meerut, UP Email: [email protected] 7. Future of Face Recognition: A Review Shwetank Arya, Neeraj Pratap, Karamjit Bhatia Department of Computer Science, GKV, Haridwar, India 8. SURVEY OF AUTOMATIC FACIAL EXPRESSION RECOGNITION BASED ON CLASSIFICATION SCHEMES P. Poornima, S. Radhapriya Research Scholar, 2Assistant Professor, Department of Computer Science, Government Arts College, Coimbatore 9. Automatic Analysis of Facial Actions: A Survey Brais Martinez, Member, IEEE, Michel F. Valstar, Senior Member, IEEE, Bihan Jiang, and Maja Pantic, Fellow, IEEE 10. A Review Paper on FACIAL RECOGNITION AnkurBansal, MukeshAgarwal, Anima Sharma, Anindya Gupta B.Tech,CSE, JECRC, Jaipur, Rajasthan, India, ([email protected]) Associate Professor, CSE, JECRC, Jaipur, Rajasthan, India, ([email protected]) Senior Lecturer,CSE, JECRC, Jaipur, Rajasthan, India, ([email protected]) Senior Lecturer, CSE, JECRC, Jaipur, Rajasthan, India, ([email protected]) 11. METHODOLOGY AND EXTENSIONS OF LOCAL BINARY PATTERN: A SURVEY CHANDRAJA D Department of Electrical Engineering (UG Student), BITS Pilani Hyderabad E-mail: [email protected] 12. A Survey on Different Face Detection Algorithms in Image Processing Doyle Fermi, Faiza N B, Ranjana Radhakrishnan, Swathi S Kartha, Anjali S U.G. Student, Department of Computer Engineering, Model Engineering College, Thrikkakara, Cochin, India Assistant Professor, Department of Computer Engineering, Model Engineering College, Thrikkakara, Cochin, India 13. Detailed Survey of Different Face Recognition Approaches Yogish Naik Department of Studies in Computer Science and MCA, Kuvempu University, Karnataka 14. Detecting Faces in Images: A Survey Ming-Hsuan Yang, Member, IEEE, David J. Kriegman, Senior Member, IEEE, and Narendra Ahuja, Fellow, IEEE

15. Face Recognition: A Survey Muhammad Sharif, Farah Naz, Mussarat Yasmin, Muhammad Alyas Shahid and Amjad Rehman Department of Computer Science, Comsats Institute of Information technology WahCantt MIS Department CBA Salman bin Abdulaziz University Alkharj KSA 16. A Survey on Sentiment Classification in Face Recognition JingyuQian1 Engineering School, University of Michigan, Michigan MI 48105, US [email protected]