Performance Evaluation of Multimodal Biometrics System

Transcription

1 Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Performance Evaluation of Multimodal Biometrics System 1 A.S. Raju and 2 V. Udayashankara 1 Department of Electronics & Instrumentation Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India. asraju74@gmail.com 2 Department of Electronics & Instrumentation Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India. v_udayashankara@sjce.ac.in Abstract Biometric authentication has attracted several researchers due to its importance in security applications. Literature lists various unimodal and multimodal authentication systems. In this work, a multimodal biometric system is presented that makes use of Electrocardiogram (ECG), Face recognition and Fingerprint traits which is robust to spoof attack and liveliness detection. Face and Fingerprint feature extraction are computed by Central Symmetric Local Binary Pattern (CS-LBP) and Local Binary Pattern (LBP) respectively. Amplitude and interval features are selected for ECG recognition. A multimodal biometric database with face, fingerprint and ECG biometric features has been collected for 50 users and the biometric system is built using feature level fusion. The trained fused (ECG, Fingerprint and Face) multi-biometric features are compared with the test features using Euclidean distance for authentication. Experiments on the acquired database of ECG, Face and Fingerprint recognition yields False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) values significantly better compared to unimodal authentication system. Index Terms:Feature selection, ECG, fingerprint, face recognition, multimodal biometric system. 367

2 1. Introduction An individual can be automatically recognized using biometric technology based on the behavioral and biological characteristics. A biometric characteristic can be either behavioral or biological property of an individual. By using distinguishing and repeatable biometric features automated recognition of individual can be achieved. For example, fingerprint, face recognition, iris recognition etc. Biometric features are stored for the purpose of comparison in the form of templates. During the recognition process, the actual biometric is compared with the stored template. There are two main steps in biometric authentication, Identification process: The biometric features are compared with several stored biometric traits. Verification: The biometric features compared with only one biometric trait stored in the system. Identification and verification become equivalent if single biometric trait is stored in the system. Otherwise, biometric verification process is a limited version of biometric identification. Human physiological and/or behavioral characteristics can be used as biometric features if it satisfies the following criteria. Universality: The Universality requirement refers to any physiological or behavioral characteristic that every individual should have. Distinctiveness: The Distinctiveness refers to any two persons should sufficiently differ in characteristic. Permanence: The Permanence requirement refers to the characteristic that should be sufficiently in-variant over a period of time. Collectability: The Collectability refers to the characteristic that should be measurable. The human identification and verification can be achieved by an important factor - Biometric Recognition. There are already various biometric traits presented in today s security applications, but not all of them are used for high security applications. The most widely used biometrics is also prone to inaccuracies and can cause falsification. This paves the way for a need of novel biometric recognition. The Multimodal Biometrics System can handle multiple physiological or behavioral characteristics for identification, verification or enrollment. Some forms of biometric identification [1] include the following. Fingerprint. Face geometry. Iris. ECG (Electrocardiograph). EEG (Electroencephalograph). Voice print. 368

3 Blood vessel patterns in Hand or Retina. Signature/Handwriting Dynamics. Finger geometry. Here, a novel approach for human identification and verification based on ECG along with Fingerprint and Face of an individual is proposed. This work explores the effectiveness of individual ECG biometric with other two eminent biometric traits i.e., fingerprint and face which are known to be a least conspicuous for efficient individual authentication. Unimodal biometric system is neither secured nor can it achieve the optimum performance. However, combining three different modalities of biometrics, offer advantage for user authentication [2]. 2. Various Biometrics: Ecg, Fingerprint & Face This section gives the details of various basic parameters related to individual biometrics traits. ECG The illustration of electric potentials which are responsible for the normal functioning of heart activity and its various parameters leads to ECG, in which the main bioelectrical activity happens by the functioning of cyclical contractions and relaxations of the heart muscles. An average cycle of ECG yields the particular waves or parameters with respect to atrial or ventricle depolarization and/or repolarization. The most prominent bioelectric features of an ECG show the evidence lying in the P, Q, R, S and T waves. The subject shows the dissimilar patterns in their ECG signals, because of change in individual morphology, time duration and range of amplitudes with respect to their heartbeats. The uniqueness of ECG signals within the individuals happens for the reasons like dissimilarity in size of heart muscle, position, and physical state of their heart. Figure 1 gives the details of standard ECG signal and its parameters; it indicates physiological signal with its inherent feature of liveliness that signifies the life signs. The feature of ECG guarantees an individual to be present in person at the time and place of enrollment. Thus, the use of ECG signal for biometric purpose is resistant to spoof attack and also ensures the robustness in biometric system. It is mimic proof and hard to replicate or stolen. Therefore, the ECG has the tough credential to successfully handle the privacy and security issues of an individual [3]. Pre-Processing In the view of signal analysis, pre-processing of ECG signal is important. Its aim is to suppress the noise and artifacts present in the signal. The ECG signal when acquired will be mixed with the 50 Hz interference signal. This leads to 369

4 error in feature values when not removed. Hence there is a need for preprocessing Feature Extraction The features in an ECG signal are many. Here, the amplitude, angle and few interval features are estimated. The heterogeneity of ECG signals among individuals can be due to the variation in size, position and physical condition of their heart. Hence, for a particular person these features are constant. Amplitude Features The pre-processed ECG signal is applied with wavelet transform. A window of certain time period is considered and the highest peak in it is identified. R peak holds the maximum amplitude in the ECG signal and is marked as R peak. Figure 1: Standard ECG Signal Parameters The R peak location is recorded and is preserved as Rloc (location of R peak).similar procedure is repeated to all the cycles of the ECG sample and R peak, Rloc values are stored. The P peak is available before R peak in the time slot of ms. By using window, the peaks are analyzed in respective time intervals. The location corresponding to P peak is stored as Ploc. In the similar manner Q, R, S and T are also extracted. The waves extracted from the ECG are marked on the ECG signal [4], [5]. Angle Features The angle subtended at the peaks in ECG signal can be utilized for the purpose of biometric recognition. Here three such angle features are used: angle PQR, angle QRS and angle RST. Mathematical concepts of finding the angle between two lines are used to calculate the angle. [6]. 370

5 ECG Signal ECG 1 ECG 2 ECG 3 ECG 4 ECG 5 Table I: ECG Amplitude Features R Peak Q Pea k S Peak T Peak P Peak Table I illustrates ECG signal parameters such as P, Q, R, S and T Peak amplitude features and its values, for ECG samples from acquired database. Interval Features These are another set of features that can be used for Biometric recognition. ECG possesses several interval features. These features are at peak to peak intervals, namely, RP, RQ, RS, RT and RR intervals [7]. Table II: ECG Interval Features ECG Signal QRS Interval P-P Interval P-R Interval R-R Interval Q-T Interval ECG ECG ECG ECG ECG Table II illustrates ECG signal parameters such as QRS, PP, PR, RR, QT interval features and its values, for ECG samples acquired from our database. Table III: ECG FAR/FRR/TSR Values Threshold FAR FRR TSR Table III illustrates ECG accuracy parameters such as false rejection rate, false acceptance rate and Total success rate values with respect to different threshold value, for ECG samples from acquired database. To verify the performance of 371

6 ECG based authentication system, angle and interval features are extracted from the ECG samples selected randomly from database. The algorithm is tested on all the three quantitative measures (FAR, FRR and TSR), various performance parameters are considered and computed. The FRR, FAR and TSR are computed to determine the effectiveness of the proposed algorithm which is good. Figure 2 depicts the graphical representation of FAR and FRR values for acquired ECG database. It indicates the threshold values against to error % of the ECG parameters. It can be observed that the FAR and FRR values for ECG biometric authentication are better in real time database. Table 4 shows the performance results of ECG algorithm, which depicts all the values of various performance parameters for the acquired ECG database. Accuracy, sensitivity and specificity are also good. Hence this method can be used in real time biometric recognition systems. Fingerprint Figure 2: FAR/FRR for ECG Database Table IV: ECG Accuracy Parameters 1 Accuracy Sensitivity Specificity Positive Predictive Value(PPV) Negative Predictive Value(NPV) False Positive Rate (FPR) False Discovery Rate (FDR) False Negative Rate (FNR) Fingerprint represents the feature pattern of a finger. With evidence, it is strongly believed that every fingerprint is unique. The manual classification of fingerprint is time prone to errors and time consuming. The very first scientific paper related to fingerprint recognition was published in 1864 and the first automatic fingerprint identification system (AFIS) was introduced in Since then, there is a rapid progress has been made in recognition rates [8]. 372

7 Figure 3: Fingerprint Image Figure 3 shows a standard fingerprint representation. It consists of ridges and furrows. These ridges and furrows present good similarities in each small local window, like parallelism and average width between ridges. However, literature indicates that, fingerprints are not distinguished minutia (abnormal points on the ridges), not by ridges and furrows. A variety of minutia is presented in literature. Among them, two are most significant and is used to larger extent: Termination represents the immediate termination of ridges; Bifurcation, is the point on the ridge from which two branches deriving [9]. Local Binary Pattern The Local Binary Pattern technique assigns label to every pixel in an image by means of thresholding. This is achieved by using a 3 3 neighborhood system with the help of equation 1. LBP 7 ( X, Y p p ) ( )2 m m s g g (1) p 0 p m where, g is the intensity of central pixel, gm is neighborhood pixel intensity, p p indicates number of pixels in neighborhood on circle of radius R. The sign function sx ( ) is given by, 1 forx a sx ( ) 0 forx a Fingerprints consist of micro patterns that can be better described by LBP operator. It is highly discriminative and has less computational complexity. (2) Figure 4: LBP Feature Extracted Histogram of Live Fingerprint 373

8 Figure 4 shows the histogram of LBP feature extracted from a live fingerprint. The matching of image pair is done by calculating the distance between two LBP feature histograms. Lesser the distance between histograms indicates more similarity in images. The algorithm for matching a partial and full image pair is based on distance between two LBP feature histograms [10]. Table V illustrates Fingerprint accuracy parameters such as FAR, FRR and TSR values with respect to threshold value, for Fingerprint samples acquired from our database. Table V: Finger Print FAR/FRR/TSR Values Threshold FAR FRR TSR 1.0e e e e e e e Table 6 shows the performance results of Fingerprint algorithm. It depicts the values of performance parameters for the acquired Fingerprint database. The FAR and FRR values for Fingerprint biometric authentication are better in real time database. Accuracy, sensitivity and specificity are also good. Hence this method is applicable for real time biometric recognition systems. Figure 5 depicts the graphical representation of FAR and FRR values for acquired Fingerprint database, it indicates the threshold values against to error. Figure 5: FAR/FRR for Fingerprint Database 374

9 Face Table VI: Finger Print Parameters 1 Accuracy Sensitivity Specificity Positive Predictive Value(PPV) Negative Predictive Value(NPV) False Positive Rate (FPR) False Discovery Rate (FDR) False Negative Rate (FNR) Common and natural way of identifying a person is by face recognition. It distinguishes between two persons. Several features that can be used for recognition involves nose, lips, eyes etc. It is a non-invasive process where prominent portion of individual s face is considered and is converted to its digital equivalent. A better image source like a good resolution camera and scanner is used for better accuracy. Most of the facial recognition systems are designed to work with gray-scale images. Figure 6 shows the facial image representation of different individual [11]. Figure 6: Individual Facial Images The recognition problems finally depend on the representation of template. A unique and simple template set provides better identification and verification process. In biometric based individual authentication system, physical and behavioral characteristics like voice, signature, iris and fingerprint recognition etc are used. But, a main challenge is to make the system safer by avoiding spoof attacks. It is essential to ensure the vitality detection from the biometric sample to be used in order to protect the system from spoof attacks. A good biometric is characterized by use of highly unique features. It reduces the chance of two persons having similar characteristics and also prevents the misinterpretation of feature. [12]. The recognition problems, either verification or identification, depends on the representation of templates. Oneto-one verification or one-to-many identification cases will be easy and straight forward iff templates remain unique and simple. An actual measurement of the biometric sample collected from a legitimate and live individual improves the reliability of a system because it enables the system to reluctance against artifacts to be enrolled. In most of biometric related authentication system, it is 375

10 difficult to ensure the vitality signs they possess, instead these identifiers are not confidential. Using ECG as standalone verification for biometric purpose may not provide sufficient accuracy; instead, a combination of ECG with other sorts of biometric methods will increase the discriminative information about an individual. The ECG provides additional information to an unobtrusive biometric such as fingerprint and face. In this work, it is being shown that, after combining ECG with fingerprint and face biometrics traits, the performance of authentication process is enhanced and also increases the robustness against spoof attacks. Center-Symmetric Local Binary Pattern The LBP based face description involves following process: A facial image is partitioned into local regions and LBP texture descriptors are extracted from each of these regions separately. The global description of face is obtained by concatenation of descriptors as shown in figure 7. The LBP operator produces long histograms and hence it is difficult to make use of it in the context of a region descriptor. To address this issue, a modified process of comparison of pixels w.r.t neighborhood is proposed. A centersymmetric pairs of pixels are considered for comparison and is shown in Figure 7. This reduces the number of comparisons by two for same number of neighbors. It can be observed that, for eight neighbors, LBP generates 256 (28) different binary patterns, whereas in CS-LBP it is 16 (24). Further, the robustness on flat image regions is obtained by thresholding the gray level differences with a small value T [13]. Figure 7: Face Description with LBP Histogram from Each Block and Feature Histogram CS-LBP can be computed by, p 1 p i CS LBP 2 s( g g ) ( )2 i 1 i i 2 (3) 1ifx 1 sx ( ) 0otherwise (4) where, g and g ( p/ 2) are the gray level values of center-symmetric pairs of i i pixels with N equally spaced pixels on a circle of radius R. It can be identified that the CS-LBP is related to gradient operator very closely. This is because some of the gradient operators consider the intensity differences between the opposite pixels of neighborhood. In this paper, our consideration is about region description and there will be no further discussion about operator level comparison of LBP with CS-LBP [14]. 376

11 Table VII illustrates Face accuracy parameters such as FAR, FRR and TSR values with respect to threshold value, for Face samples acquired from our database. Figure 9 depicts the graphical representation of FAR and FRR values for acquired face database. It indicates the threshold values against to error. The FAR and FRR values for face recognition based biometric authentication are better in real time database. Accuracy, sensitivity and specificity are also good. Hence this method can be used in real time biometric recognition systems. Table 8 shows the performance results of face algorithm, which depicts all the values of various performance parameters for the acquired face database. Table VII: Face FAR/FRR/TSR Values Threshold FAR FRR TSR 8.0e e e e e e e Figure 8: FAR/FRR for Face Database Table VIII: Face Accuracy Parameters 1 Accuracy Sensitivity Specificity Positive Predictive Value(PPV) Negative Predictive Value(NPV) False Positive Rate (FPR) False Discovery Rate (FDR) False Negative Rate (FNR)

12 3. Multimodal Biometric System Unimodal biometrics often cannot meet all the system requirements; therefore combining multiple biometrics can overcome the limitations of unimodal biometrics and increases the overall performance of the system. In this section we will discuss the advantages of fusion and explore the different types of fusion in multi-modal biometric systems. As it is shown in the previous section, the performance of the ECG biometric system is very sensitive to different factors which are a challenge for practical applications. Although the proposed method has a better performance compared to state-of-the-art techniques, still it is not accurate for many applications. We will specifically discuss the fusion of ECG with fingerprint, Face and propose a new sequential fusion system. The fusion of the three biometrics is beneficial and the combined system provides live detection and performance improvement for both the unimodal systems. Multimodal biometrics combines information from different sources as opposed to unimodal biometric system [15]. In multimodal biometric systems fusion is done at various levels like feature level, decision level and the score level. Each method of fusion is briefly explained below [16]. Feature Level Fusion at this level is done by combining more than one feature set extracted from several data sources that generate a new feature set to represent an individual as in figure 9. If the set of features taken from one biometric is independent of another biometric, then it is better to combine two vectors to form a new single vector, if the features of that biometrics stand under same kind of measurement scale. Feature Matching: The features obtained from ECG biometric, Face biometric and Fingerprint biometric are combined for matching [17]. The trained fused (ECG, Face and Finger) multi biometric features are compared with the test features using Euclidian distance for authentication. The fused feature with the minimum Euclidean distance is preferred if it is less than the threshold value. Tested feature get rejected otherwise. Euclidean distance can be calculated using the following formula. d ( x, w ) ( x w ) (5) x k k Figure 9: Block Diagram of Feature Level Fusion 378

13 4. Experimental Results The proposed algorithm is a multimodal biometric recognition system that involves ECG, Fingerprint and Face. Samples of all the biometric are acquired and tested for 50 subjects. Table IX shows the performance results of multi-modal algorithm, which depicts all the values of various performance parameters for multi-modal acquired database. The fusion of the acquired database from ECG, Face Recognition and Fingerprint are achieved. The FAR and FRR values found to be much better in comparison with those of the unimodal verification. It indicates that, the fusion process provides better authentication with minimal error [18]. Table X shows the performance results of unimodal (ECG, Fingerprint and Face) and multi-modal algorithm, which depicts all the values of various performance parameters for respective acquired database. It can be observed that the proposed multimodal system yields better accuracy (73.460) as compared with unimodal system. Similarly the experimental results of sensitivity, specificity and other parameters are also good. Table IX: Multi-Modal Biometric Accuracy 1 Accuracy Sensitivity Specificity Positive Predictive Value(PPV) Negative Predictive Value(NPV) False Positive Rate (FPR) False Discovery Rate(FDR) False Negative Rate(FNR) Table X: Performance Results of ECG, FACE, Finger Print and Multi Model Parameters ECG FACE Finger Print Multi-model Accuracy Sensitivity Specificity Positive Predictive Value(PPV) Negative Predictive Value(NPV) False Positive Rate (FPR) False Discovery Rate(FDR) False Negative Rate (FNR)

14 5. Conclusion The biometric recognition using ECG, fingerprint and face are implemented. The performance evaluation is recorded on acquired 50 subjects database. The extracted features are stored and corresponding scores are generated, and tests are performed for various biometric parameters of the captured database. The recognition rates were computed with respect to FAR and FRR values which are calculated separately for all modalities shown previously. The algorithm evaluates 50 subjects and a variety of match rates have been obtained for different features in the verification. The testing on acquired fingerprint and face database is performed. The accuracy of fingerprint and face are satisfactory. The FAR for ECG are good compared to others. The high value of FAR and FRR tabulated here for the acquired database is attributed to poor quality images. However, for a standard database the methods would give better results. It is observed that compared to unimodal biometric authentication system, multimodal algorithms yields improved results. Our future focus will be to develop a real-time multi-modal biometric system for larger database and to provide security by preserving the privacy of the biometric template. References [1] Pradeep K.A., Anwar Hossain M., Abdulmotaleb E.S., Mohan S.K., Multimodal fusion for multimedia analysis: a survey, Multimedia systems (2010), [2] Boban D., Mathew C.M., Detecting surgically altered face images using CS-LBP and genetic algorithm, International Journal of Advanced Research in Computer Science and Software Engineering 4(8) (2014), [3] Brunelli R., Falavigna D., Person identification using multiple cues, IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10) (1995), [4] Samik C., Madhuchhanda M., Saurabh P., Biometric analysis using fused feature set from side face texture and electrocardiogram, IET Sci. Meas. Tech., 11(2) (2017), [5] David Pereira C., Ana L.N.F., Figueiredo, One lead ECG based personal identification using ziv-merhav cross parsing, International Conference on Pattern Recognition (2010), [6] Nahid G., and Reza B., Reliable features for an ECG-based biometric system, Iranian Conference of Biomedical Engineering (ICBME2010) (2010). [7] Hong L., Jain A.K., Integrating faces and fingerprints for personal identification, IEEE Trans. PAMI 20(12) (1998). 380

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