doi:10.2498/iti.2012.0455
Face Recognition Implementation for Client Server Mobile Application using PCA Emir Kremic1, Abdulhamit Subasi1, and Kemal Hajdarevic2 1 International Burch University, Faculty of Engineering and Information Technology, Sarajevo, Bosnia and Herzegovina 2 University of Sarajevo, Faculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina E-mail(s):
[email protected],
[email protected],
[email protected]
Abstract. For the past few years in the face recognition research area are made very progressive improvements. This is because of the high level and versatile technologies in use nowadays, and high level of processors running on our machines and mobile phones. Available technologies provide mechanisms which use face recognition for security identification (user face) and authentication purposes. The aim of this paper is to present and propose client – server model and to compare it with the most recent client – server models for face recognition with a GPG infrastructure which uses security private key (symmetric encryption) with main purpose to securely transmit image (user face) over the network. Moreover in the face recognition algorithm is implemented Principle Component Analysis (PCA) algorithm for face recognition. Proposed system has been tested on the mobile phone with Android OS platform, using previous research experiences where system was initially developed for DROID emulator. The implementation of the PCA is done on the MATLAB side.
Keywords. PCA, Network Security, Android, face recognition
1. Introduction Most modern mechanism for face recognition relies on artificial Intelligence. The task of artificial intelligence in our case is to try to work out the problem of face recognition over mobile phone. The task of artificial recognition is about computing automata or models which are capable of retrieving data from images and therefore we say, that image is any format consisting of visual data, static images, video sequences or video sequences combined from more than just one camera [8]. We present here
some of examples of artificial recognition model [4,5,6,7]: -
Controlling process: industrial robot, or autonomous vehicle Monitoring events: video surveillance, people counting Organization of information: indexed database which consists images Object modeling and environment: analyses of medical images Interaction: user and computer interaction
Usually face recognition is a part of biometric recognition system, and is based on verification of input data [7]. The artificial model for recognition is considered to be a complementary discipline of biological view. The biological form teaches us about visual perception of humans and animals and presents models that explain how the flow of systems works and process. The sub – domain are: scene reconstruction, process monitoring, recognition of humans or subjects, prediction and reconstruction of destroyed images. This paper applies its research to the field of artificial face recognition for mobile security, authentication and identification using the client – server communication.
2. The Authentication’s techniques Simulating the computer system to check the person’s identity against another person, we are beginning the process of authentication. There are two essential type of the authentication process and those are: Verification This is a process of confirming identity of any person by comparing the input data with ones existing in database. This is 1:1 authentication method [7, 8].
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Identification
In this case we are matching the input data with all samples in the database with a view to retrieving the data related for the person. This system represents the 1: N authentication model [7, 8]. The authentication methods are the key in confirming someone’s identity.
3. The methods of authentication There are three methods of authentication [8]: 1. Based on Knowledge[8]: This method is based on user’s memorization of data. The most frequent example is the Personal Identification Number (“PIN”). The significant security gap of this approach is made by user, since they are providing the PIN’s which sometimes can be easily predicted and changed or it can be written on the paper. 2. Based on Object[8]: This method is based on who is going to confirm the person’s identity. These are: ID, Passports, credit cards, etc. It is good because the persons do not require any memorization. But the identification card can be lost, stolen or fraud. 3. Based on Biometry[8]: The user has some biometric identifier: finger print, face detection, retina recognition, infrared face radiation and possible other. Relative to those methods, the process of authentication can be implemented and, depending on the system, by authenticating we can retrieve different kind of data related to the person if the person exists in database. In here, fraud is unlikely to occur; by contrast in the previous two methods, it is much more likely to do.
4. The Importance of Biometry As we have mentioned previously, there exist three different identification methods today. The first one is ID card. The second one is based on knowledge and memorization (PIN). The third model is based on biometry - face recognition which we will discuss in this paper. This approach (face recognition) shares same goal: to confirm the person identity. It is a much more secure way than user password. Face recognition approach has much more potential to become an indispensable method of identification. The approach based on confirming the person’s identity based on the face characteristics has increase this research area, because the problem
related to security is much more acute today than before. Face recognition model is a more precise way of identification. Human factors are still presenting the main shortcoming in security. What was before presented as science fiction today is reality. In the exploration, we show the segment of biometry for mobile security [2]. The aim is to enable the mobile user to enter the date, or mobile phone, by using the mobile face recognition model of security. This research opens the door to more sophisticated future research. Yet in here, the testing is combined with MATLAB and DROID, where model has been tested. The subject of face recognition has its own research in the area [6] of neurocomputing, security, pattern recognition, neural networks, machine learning etc. The theoretically proposed model of this subject is by with application. Beside the model which will be presented here, the face recognition biometry in the future can be implemented for passport identification usage and other applications [5]. All of data will be in one place. The benefits that would accrue are [8]: • • • • • •
Lower administration costs Identifications integrity Information’s integrity Efficient access to data Quicker service handling Higher level of security
All the above – mentioned advantages are but a part of the overall benefit associated with replacing the traditional model of security with biometry. Even though we consider this approach as a new security application, we cannot consider it as it is our own research. Many years ago, there was a Greek mathematician who proposed the Pit agora’s Theorem. The extended version of Pitagora’s Theorem was the Euclid distance [3]. The mathematical model of Euclidian Distance [3] was applied to the model of face recognition. The Principle Component Analysis PCA model recognizes faces by applying the mathematical model of Euclidian Distance. Even though we are living in a digital world, we could agree that, in the modern world, we have just done the transformation from one perspective to another. This paper intends to erect the modern transformation and application, on an extended model of the previous mathematical models which are applicable to face recognition.
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Figure 1: Wireless and Web Architecture [1]
5. Earlier Model for Client Server vs. the Implemented One Earlier with the extension of the internet and client – server methods [1] was introduced the thesis “Client – Server Computing in Mobile Environment” [1, 8]. They examined the range of services and application provided by mobile system. They examined the paradigm of mobile computing since the advance model in wireless networking technology was introduced. In this paper, the author has taken into consideration mobile – aware adaptation. That means they have thought about computational complexity and resources for building mobile systems and applications. The research was continued and many different algorithms were introduced for face recognition. Some of them are Neural Networks, PCA, LDA, SVM, etc. Many studies [7, 8] in distinguishing mobile client – server computing including mobile – aware adaption, extended client server model and mobile data access. The model shown in Figure 1, was introduced in thesis “Connecting mobile workstations to the Internet over a digital cellular telephone network [1]. Figure 1 presents a similar approach which supports Web Applications over wireless links. The HTTP agent and proxy HTTP are applied to: a) Unnecessary message exchange: reduced b) The volume of data transmitted over the wireless link: reduced c) Support disconnected operations [1]
6. Mobile Authentication: State of the Art The aim of the proposed authentication model is to reduce fraud with cell phone authentication. This approach is about to become very important segment of security authentication, where a new generation of mobile phone plays a major role in service industries such as [8]: Banks: Federal Financial Institutions Examination Council (FFIEC) has presented guidelines [8, 11] for the financial sectors to start with a new authentication methods interdicting illegal transaction. Using the new generation of mobile technologies, there cost – effective approaches. The main issue becomes the security between client and bank. The face – recognition model in the future will potentially reduce authentication as weakness for fraud. . Mobile Network Operators: This method depends on the authentication concept. The Mobile Network Operator (MNOs) has an opportunity to present a new approach to security, i.e., a functional authentication security model for the benefit of users. Technology providers: The aim of technology is to follow current business requirements. Vendors need to be encouraged to adopt new products and to modify existing solutions to meet the demands and requirements of their customers such as: banks, merchants, etc. Having multiple authentication techniques strengthens the system within wider frameworks. It empowers the system to redress potential weaknesses.
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Figure 2: The Proposed and implemented mobile face recognition schema using client server architecture
Face – recognition approach presents the most transparent authentication model of authorized systems in use [7, 8]. Applying the current model using artificial intelligence approach [7, 8], the process of authentication is no longer a matter of providing a pass or fail response. The high level of confidence will enable users to access the full
7. Architecture model In Figure 2 is shown an authentication model which is used in this paper to evaluate system capabilities. As it is shown it is presented: Cellular Phone Side and Matlab Side [7, 8]. The connector between these two is Tomcat Server. On the Cellular Phone Side is Java Application. On the Server side is Matlab consisting of: user database, authentication engine, biometric profile and authentication manager. Authentication manager has overall control of the authentication system, deterring both when authentication should take place and what is the current state of security [7]. Authentication engine, authenticates users, a Biometric Profile generate and train the relevant biometric template. Database contains information about users, compatibility, information about which mobile devices are configured to work with the architecture, along with a list of supported biometric system, in our case it is PCA. Mobile phone is via wireless connected with a server side and it communicates to standard wireless protocols 802.11 (CSMA/CA).
The significant improvements were made in architecture design comparing the [7, 8]. The implemented model comparing to the proposed in [1], is improved and it provides the user security beside the face recognition method. The model implements the public key repository using PKI (Public Key Infrastructure) which is used to guarantee image authenticity when image is sent to the server over the WAN network. The ambition is to extend the network improvements from the one presented in [7]. It enables us to avoid the potential attacker to access the data repository where biometric profile are stored or even to access the biometric signature while biometric profile is sent over network [10]. Such a problem in accessing the insecure network is known as man – in – the – middle – attack [10]. Consequently the model shown in Figure 2 is providing a secure network, while in [7] can be considered as insecure network.
8. Technical Aspects System consists of two different parts, the server part and the face recognition part as follows [7, 8]: a) Mobile Platform – System is embedded into Samsung Galaxy S model of mobile phone and supports Java technology and is integrated with Java.
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b) Android Mobile Technology – Java SDK and Android technology, with DROID emulator c) Hosting Web Server – A server is required to host the application. This application will transmit and receive data over the Internet. Tomcat Apache HTTP Server is used as the web server. d) Database – Small database is developed for testing and evaluation. Database consists of face images. e) Face Recognition Method – Face Recognition part is developed using Matlab. It is integrated with the server. f) Public Key Repository – the Public key repository is provieded on the network. The public keys are stored in public key repository and the goal is to secure the biometric data when users send biometric profile over network. It confirmes the biometric unique signature that is sent over network.
the females. One of the reason females are having more elements when image is PCA feature extracted than males. They have more hair and it becomes confusing for the machine to recognize it. Also, we can see from Figure 5 that females with lighter hair are having a higher Euclidian distance. The matching pixels becomes very similar due the hair lightning and it is problem for the PCA when it compare it.
Figure 4: Male vs. Female Face Recognition
9. Empirical results The test case contains 34 persons and each person has 20 different face images. The sample of the training and testing database is shown in Figure 3. The test is projected onto eigenfaces – PCA. We have calculated the Euclidean distance between test images and training image and have found the closest Euclidian distance.
Figure 5: Face recognition results females with light hair and dark hair
of
10. Conclusion Figure 3: Database sample
A threshold is set such that if the closest distance above the threshold, the test face is considered unrecognized, and if below, is associated with the identity of the closest face. With implementing PCA we have achieved on average accuracy of 88.88%. Figure 4, graphically presents the PCA Recognition algorithm, comparing Females vs. Males; whereas Figure 5, shows comparison of females with dark and light hair. As we can see from the Figure 4, the experimental results show that male are having lower Euclidian distance compared to
Increased processing power, storage capacity of mobile phone devices in real–time face recognition for mobile phones are no longer unattainable, [4] There are many popular implementations with high performance mobile phones as: Apple’s, iPhone, Google’s Android and RIM’s Blackberry. This thesis is developed and tested on Android Samsung Galaxy S [9]. The application built is identity authentication for access control and prevention of unauthorized mobile phone usages. The future work would require more testing and the continuation of face recognition process improvements to extend the face recognition rate to a higher accuracy. The work would acquire the research in training the
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machine model to have higher matching accuracy when matching females with light hair and to proposed better result from one we have now. In the proposed model as described in Figure 2 it is achieved overall accuracy of 88.88%. In this paper we have presented the improvement in client server implementation for mobile face recognition. Such improvements were made by adding the privet key to have secure network model when transmitting the biometric data over the network. Even though we have done significant research, there is still open room for continuing working on this subject.
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Arabia. Naif Arab University for Security Science; 2010. p. 374-379. [8] Kremic E. Face Recognition: Advanced Security Model for Mobile Phones. M.Sc. Thesis. Sarajevo; International Burch University; 2011 [9] Samusing Galaxy S GT-19000. More out of life Samsung Galaxy S. Internet; 2011. http://www.samsung.com/uk/consumer/mobi le-devices/smartphones/android/GTI9000HKDXEU [01/12/2011] [10] Stallings W. Network Security Essentials. Pearson International Edition, ed:3rd, Upper Saddle River, New York;2007. p.83-84. [11] Federal Financial Institution Examination Council. Authentication in Internet Banking Environment. Internet; 2011. http://www.ffiec.gov/pdf/authentication_guid ance.pdf [01/12/2012]
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