Available online at ScienceDirect. Procedia Computer Science 92 (2016 )

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1 Available online at ScienceDirect Procedia Computer Science 92 (2016 ) nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-2016) Srikanta Patnaik, Editor in Chief Conference Organized by Interscience Institute of Management and Technology Bhubaneswar, Odisha, India Review paper on applications of principal component analysis in multimodal biometrics system Chhaya Sunil Khandelwal a, Ranjan Maheshewari b, U.B.Shinde* a Department of Electronics and Telecommunication, Jawaharlal Nehru Engineering College, Aurangabad,431005, India. b Department of Electronics,Professor, RTU, Kota(Rajasthan), , India. *Department of Electronics and Telecommunication, Principal CSMSSCOE, Aurangabad, , India. Abstract Unimodal biometric systems are susceptible to a variety of problems such as noisy data, intra-class variations, limited degrees of freedom, non-universality, spoof attacks and unacceptable error rates. Some of these limitations can be addressed by deploy multimodal biometric systems that integrates the evidence presented by multiple sources of information The proposed system provides effective fusion scheme that combines information presented by the multiple domain experts based on the Rank level fusion integration method, thereby increasing the efficiency of the system which is not possible by the unimodal biometric system. The proposed multimodal biometric system has a number of unique qualities, including principal component analysis and fisher s linear discriminate methods for individual matchers authentication. The novel rank level fusion method is used in order to consolidate the results obtained from different biometric matchers The Authors. Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license ( Selection and peer-review under responsibility of scientific committee of Interscience Institute of Management and Technology. Peer-review under responsibility of the Organizing Committee of ICCC 2016 Keywords: Multimodal biometrics; PCA; Fingerprint Recognition; Iris Recognition; Minutiae Extraction. * Corresponding author. Tel.: ; ; ,fax: address:chhaya.khandelwal@rediffmail.com, ranjan@rtu.ac.in, drshindeulhas@gmail.com The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the Organizing Committee of ICCC 2016 doi: /j.procs

2 482 Chhaya Sunil Khandelwal et al. / Procedia Computer Science 92 ( 2016 ) Introduction Biometrics has drawn wide acceptability during the last 35 years. It is used for building and store access control, image identification, observation and computer interfacing. The key issue of these applications is the identification of individuals by their physiological or behavioral characteristics (e.g., face, fingerprint, iris, signature, or gait)[1][2]. Each biometric characteristic has its own strengths and weaknesses: but, none is free from any one or more issues such as noisy data, non universality, spoof attacks, and unacceptable error rates. In the past few years, researchers have more and more focused on the possibility of including multiple sources of information. A simple biometric system consists of four basic components: A simple biometric system consists of four basic components: 1) Sensor module which acquires the biometric data; 2) Feature extraction module where the acquired data is processed to extract feature vectors; 3) Matching module where feature vectors are compared against those in the template; 4) Decision-making module in which the user's identity is established or a claimed identity is accepted or rejected. Any human physiological or behavioural trait can serve as a biometric characteristic as long as it satisfies the following requirements:[1][2][8] 1) Universality. Everyone should have it, barring a few exceptions, like physical deformities; 2) Distinctiveness. No two individuals should have the same characteristics; 3) Permanence. It should be invariant over a given period of time; 4) Collectability. The feature should be sensed the given system. 2. Literature survey Multimodal techniques are not new to the medical world. In routine medical checkups also, it is often preferred have a primary and a confirmatory examination. The inclusion of evidences from more than one sources would enhance the overall Accuracy of the system. Table 1. Literature Survey. Author Biometric Modalities Level of fusion Accuracy reported Vincenzo Conti et al. [3] Fingerprint and iris Feature level fusion 96% Abhishek Nagar et al. [4] Iris, fingerprint and face Feature level fusion 97% Robert Snelick et al. [5] fingerprint, face Simple-Sum fusion 95.5% A. Muthukumar et al. [6] Iris and fingerprint Score fusion 95.5% Sumit Shekhar, et al. [7] Iris, fingerprint and face Sparse matrices 97.5% 3. Biometric system errors The Biometrics signal acquisition is not free from errors. When errors are significant, two samples of the same biometric characteristic from the same subject (e.g., two impressions of a user s right index finger) may not exactly be the same due to imperfect imaging conditions (e.g., sensor noise and dry fingers), changes in the user s physiological or behavioural characteristics (e.g., cuts and bruises on the finger), ambient conditions (e.g., temperature and humidity), and user s interaction with the sensor (e.g.finger placement). Therefore, the response of a biometric

3 Chhaya Sunil Khandelwal et al. / Procedia Computer Science 92 ( 2016 ) matepairs nonmate pairs genuine distribution impostor P Imposter distribution False negative Decision Threshold Matching False positive Genuine distribution Fig.1. Biometric system error rates The main system errors are usually measured in terms of: FNMR (false no match rate) mistaking two biometrics measurements from the same person to be from two different persons; FMR (false match rate) mistaking biometric measurement from two different persons to be from the same person. Multimodal biometric systems can be designed to operate in five integration scenarios: 1)multiple sensors, 2) multiple biometrics, 3) multiple units of the same biometric, 4) multiple snapshots of the same biometric, 5) multiple representations and matching algorithms for the same biometric[1][4][8] 4. Fusion in multimodal biometrics A biometric system has four important modules. The sensor module acquires the biometric data from a user; the feature extraction module processes the acquired biometric data and extracts a feature set to represent it; the

4 484 Chhaya Sunil Khandelwal et al. / Procedia Computer Science 92 ( 2016 ) matching module compares the extracted feature set with the stored templates using a classifier or matching algorithm in order to generate matching scores; in the decision module the matching scores are used either to identify an enrolled user or verify a user s identity. Sanderson and Paliwal [10] have classified information fusion in biometric systems into two broad categories: pre-classification fusion and post-classification fusion. Preclassification fusion refers to combining information prior to the application of any classifier or matching algorithm. In post-classification fusion, the information is combined after the decisions of the classifiers have been obtained. The figure (2) shows the block diagram of unimodal and multimodal biometric system. In a unimodal biometric system, the first stage is the enrollment stage, next is the feature extraction stage in which the features of each person to be identified is extracted and next is the feature matching stage in which the obtained features are compared with the data base and the output is obtained.[11] In a multimodal biometric system, after the enrollment stage the images are averaged and normalized and then its given to the matching stage in which the features are matched and then it is ranked according to the availability of data and the result is obtained. (b) Fig.2.Block Diagram of (a) Unimodal and (b)multimodal Biometric System 4.1 Principal component analysis Principal component analysis (PCA) is one of the statistical techniques frequently used in signal Processing to the dimension reduction or to the data decorrelation.pca takes the advantage of Eigenvectors properties for determination of selected object orientation. PCA belongs to linear Transforms based on the statistical techniques. This method provides a powerful tool for data analysis and pattern recognition which is often used in signal and image processing as a technique for data compression, data dimension reduction or their de correlation as well. PCA

5 Chhaya Sunil Khandelwal et al. / Procedia Computer Science 92 ( 2016 ) is a statistical method which involves analysis of n-dimensional data. PCA observes correspondence between different dimensions and determines principal dimensions, along which the variations of the data is high. The basis dimensions or vectors computed by PCA are in the directions of the largest variance of the training vectors. These basis vectors are computed by solution of an Eigen problem, the basis vectors are eigenvectors. The Eigen vectors are defined in the image space. They can be viewed as images. Hence, they are usually referred to as Eigen images. Eigen image recognition derives its name from the German prefix Eigen, meaning own or individual. The first Eigen image is the average image, while the rest of the Eigen images represent variations from this averaging image. When a particular image is projected onto the image space, its vector (made up of its weighted values with respect to each Eigen image) into the image space describes the importance of each features in the image. In our system the Eigen image approach is used, because it has several advantages. In the context of personal Identification, the background transformations can be controlled and the Eigen image approach has a compact representation of an image can be concisely represented by a feature vector with a few elements. Also, it is feasible to index an Eigen image-based template database using different indexing techniques such as retrieval can be conducted efficiently. Moreover, the Eigen image approach is a generalized template-matching approach which was demonstrated to be more accurate than the attribute based approach.[2][12] 4.2 Rank level fusion approach Rank-level fusion is a relatively new fusion approach. When the output of each biometric matcher is a subset of possible matches sorted in decreasing order of confidence, fusion can be done at the rank level. The goal of ranklevel fusion is to consolidate the rank output by individual biometric subsystems (matchers) in order to derive a consensus rank for each identity using three methods to combine the ranks assigned by different matchers[13]. 1. The Highest Rank Method, 2. The Borda Count Method and 3. The Logistic Regression Method. In the highest rank method, each possible match is assigned the highest (minimum) rank, as computed by different matchers. The Borda count method uses the sum of the ranks assigned by individual matchers to calculate the final rank.. In the logistic regression method, a weighted sum of the individual ranks is calculated. The weight to be assigned to different matchers is determined by logistic regression.[6][14] 5. Conclusion The multimodal biometrics is a promising area of information processing research which is directed towards understanding of traits and methods for more accurate and reliable personal information representation for subsequent decision making and matching. In the recent years there is a significant increase in research activity directed at understanding all aspects of biometric information system representation and utilization for decisionmaking support, for use by public and security services, medical diagnostics, and for understanding the complex processes behind biometric matching and recognition. Acknowledgements The Authors are indebted to their respective institutions and also to the National Institute of Electronics and Information Technology, Aurangabad, where the first author is a registered for her research work and the other authors are registered as supervisors. Thanks to Ranjan Maheshwari sir who provided valuable

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