Normalization and Weighting Techniques Based on ... - IEEE Xplore

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 13, NO. 8, AUGUST 2018

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Normalization and Weighting Techniques Based on Genuine-Impostor Score Fusion in Multi-Biometric Systems Waziha Kabir, Student Member, IEEE, M. Omair Ahmad , Fellow, IEEE, and M.N.S. Swamy, Fellow, IEEE

Abstract— The performance of a multi-biometric system can be improved using an efficient normalization technique under the simple sum-rule-based score-level fusion. It can also be further improved using normalization techniques along with a weighting method under the weighted sum-rule-based score-level fusion. In this paper, at first, we present two anchored score normalization techniques based on the genuine and impostor scores. Specifically, the proposed normalization techniques utilize the information of the overlap region between the genuine and impostor scores and their neighbors. Second, we propose a weighting technique that is based on the confidence of the matching scores by considering the mean-to-maximum of genuine scores and mean-to-minimum of impostor scores. A multibiometric system having three biometric traits, fingerprint, palmprint, and earprint, is utilized to evaluate the performance of the proposed techniques. The performance of the multibiometric system is evaluated in terms of the equal error rate and genuine acceptance rate @0.5% false acceptance rate. The receiver operating characteristics are also plotted in terms of the genuine acceptance rate as a function of the false acceptance rate. Index Terms— Multi-biometric system, score normalization, weighting of matchers, reliability measure of matchers, anchoredmin-max.

I. I NTRODUCTION

O

VER the last decade, biometrics have been widely used for forensics and criminal investigations, which require accurate and reliable verification/identification schemes. Applications of biometric-based authentication systems are rapidly increasing in real life, e.g., computer login, online banking, border crossing, smart homes, e-voting and so on [1]. Most of the practical biometric systems are unimodal, i.e., they rely on a single source of biometric information for authentication. Unimodal systems are usually cost-efficient, but the performance of such systems degrade in some practical situations when there are noisy data, intra-class variations, Manuscript received June 6, 2017; revised November 16, 2017; accepted January 28, 2018. Date of publication February 19, 2018; date of current version April 4, 2018. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada and in part by the Regroupement Stratgique en Microlectronique du Quebec. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yunhong Wang. (Corresponding author: M. Omair Ahmad.) The authors are with the Center for Signal Processing and Communications, Concordia University, Montreal, QC H3G1M8, Canada (e-mail: [email protected]; [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIFS.2018.2807790

inter-class similarities, non-universalities and spoof attacks [2]. Multimodal systems aim to overcome some of these limitations by utilizing more than one biometric source of information [2]. Several studies have suggested that a better performance can be achieved to meet the requirement of real-world applications by integrating information from multiple biometric sources. Depending on the multiple sources of information, five scenarios are possible in a multimodal biometric system. These scenarios may be multiple sensors (e.g., optical and capacitive fingerprint sensors), multiple units (e.g., left and right thumb fingerprints of a person), multiple snapshots (e.g., three impressions of a user’s face), multiple matchers (e.g., minutiae and non-minutiae based fingerprint matchers), and multiple biometric traits (e.g., fingerprint and palmprint). Amongst these five scenarios, the system that employ multiple biometric traits, known as the multi-biometric system has received considerable attention [3]–[30], since it offers several benefits [3]: (a) lower error rate, (b) improved availability, since a missing biometric trait could be covered by the availability of more than one biometric source of information, (c) higher degree of freedom, since only a subset of the employed biometrics could be sufficient to authenticate an individual, (d) less susceptible to spoof attacks, since it may not be easy to attack multiple traits at the same time, and (e) higher robustness, since the noisy samples could be replaced by samples having discriminative information to identify a user. Fusion plays an important role in improving the overall recognition rate of a multi-biometric system. It can be done at various levels, such as at the sensor level [31], [32], feature level [4]–[8], [33]–[38], score level [9]–[19], rank level [20]–[22], [39], or decision level [23], [40], [41]. Fusion at the sensor-level is not preferable in view of the large amount of redundant information contained at this level. The amount of information at the feature-level is sufficient to identify an individual; however, fusion at the feature-level is difficult in view of the fact that the feature sets of the multiple sources may either be inaccessible or incompatible. Moreover, most vendors of biometric systems do not like to reveal the feature values obtained from their systems [11]. The rank-level fusion can only be applied for identification of an individual, and therefore, has not received much attention. Fusion at the decision-level is too rigid, since only a limited amount of information is available at this level. Fusion at the score-level is generally preferred due to the ease in accessing

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 13, NO. 8, AUGUST 2018

and combining the matching scores provided by the individual matchers. Note that at the score-level, sufficient amount of information is available to identify an individual, since it has neither too much redundant nor too little information. Our methodology focuses on fusion at the score-level for a multibiometric system because of the above mentioned advantages over fusion at other levels. In score-level fusion, multiple scores from different matchers can be combined using the simple-sum (SS) or the weighted-sum (WS) fusion rules in order to obtain a single score set in a multi-biometric system. Three issues need to be considered prior to fusing these scores [24]. First, the scores from different matchers may be non-homogeneous, e.g., one matcher may provide similarity between scores while another may provide dissimilarity scores. Second, the matching scores may be on different numerical scales, e.g., one matcher may have the range of [0.25, 0.8], whereas another may have the range of [-100, 100]. The third issue is that the multiple matching scores may have different statistical distributions, e.g., one matcher may follow Gaussian distribution while another one non-Gaussian distribution. Therefore, normalization of the matching scores is an essential task under the simple-sum or the weighted-sum rules for score-level fusion in order to address the above mentioned three issues. A number of normalization techniques for score-level fusion in multi-biometric systems have appeared in the literature [11], [13]–[16], [18], [19], [24]. Min-max (MM) [24] normalization technique is the most widely used technique, in view of its simplicity and good performance for a multi-biometric system. It transforms the matching scores into a common range of [0, 1]. The distance score can be transformed into a similarity score by simply subtracting the MM normalized score from 1. This technique retains the original distribution of the scores. However, the most notable limitations of MM are: (1) it is not robust (i.e., highly sensitive to outliers in the data used for estimation), since it depends on the extreme values of the matching scores, (2) the maximum and minimum values of scores are required to be estimated when they are unknown, and (3) it does not retain the original distribution of the matching scores for a scaling factor. Performance anchored min-max (PAN-MM) technique [14] aligns different matching scores with respect to a certain performance operating point known as the anchor. The anchor for PAN-MM is the score threshold value at the equal error rate (EER) (i.e., the rate at which both the acceptance and rejection errors are equal) of the matcher in the system. Although PAN-MM is an extension of the minmax technique, it requires a prior knowledge of the individual matchers’ EERs in a multi-biometric system. EER-independent improved anchored min-max (IAMM) technique [19] considers two parameters (namely, the mean and standard deviation) of the repeated score set in order to improve the recognition rate of the system. However, the performance of a multi-biometric system using this technique has not examined in terms of the genuine acceptance rate (GAR) at a low false acceptance rate (FAR). In a multi-biometric system, the performance of multiple matchers generally is not the same; one matcher may provide

an error rate that is higher than others. Under the SS-rule based score-level fusion, the overall performance of such a multi-biometric system in terms of the recognition may be worse than that of the matcher providing the best performance due to the highest error rate provided by the weakest matcher. In order to overcome this and to improve the overall recognition rate of a multi-biometric system, weights need to be assigned to the individual matchers taking in account the strength of each matcher. Some well-known weighting techniques for the score-level fusion in a multi-biometric system have been presented [11], [26], [42]. D-prime weighting (DPW) technique presented in [11] is based on a measure of the separation between the genuine and impostor score distributions. The higher the separation between the genuine and impostor score distributions, better is the performance of the biometric system. However, this technique is very sensitive to sample variations. The Fisher discriminant ratio weighting technique (FDRW) estimates the weights of the matchers using a ratio, referred to as the Fisher discriminant ratio (FDR) [42]. This ratio, introduced by Poh and Bengio [27], measures the seperability of the genuine and impostor score distributions in a multi-biometric system. The limitation of this technique is the sensitivity to the sample variations. The Score reliability based weighting (SRBW) technique in [26] utilizes the reliabilities of the raw matching scores to estimate the weights of the individual matchers. Although the multibiometric system using SRBW provides a better recognition rate than that of the uni-biometric system, the reliability is computed based only on the average of the score set, and the performance of the system using this technique has not been examined in terms of GAR at low FAR. In this paper, two anchor-based normalization techniques for score-level fusion in a multi-biometric system are proposed. The anchor value for the first normalization technique is obtained from the overlap region between the genuine and impostor scores, while for the second technique, it is computed by considering the overlap region between the genuine and impostor scores, as well as that of the corresponding neighbors. The proposed anchored normalization techniques do not require repeated score set or the individual EERs provided by the matchers. We also propose a weighting technique wherein the weights are estimated based on the confidence of the matching scores in order to improve the overall recognition rate of a multi-biometric system at the scorelevel fusion. The proposed weighting technique considers two differences, mean-to-maximum of genuine scores and meanto-min of impostor scores. The advantage of the proposed weighting technique over the existing ones is that it considers the genuine and impostor scores that are not close to the overlap scores. Each of the proposed normalization techniques is integrated with the new weighting technique using fusion at the score-level. The proposed techniques are evaluated for a multi-biometric system which has three modalities, namely, fingerprint, palmprint and earprint. Comparative results between the proposed techniques and existing ones are also presented using publicly available databases. The paper is organized as follows. In Section II, the two normalization techniques are proposed, while Section III

KABIR et al.: NORMALIZATION AND WEIGHTING TECHNIQUES BASED ON GENUINE-IMPOSTOR SCORE FUSION

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TABLE I M ATCHING S CORES OF 3 P ERSONS E ACH H AVING 2 S AMPLES FOR THE E ARPRINT M ODALITY (i, j = 1, 2, 3; p, q = 1, 2)

introduces the new weighting technique. Experimental results are presented in Section IV and a comprehensive comparison with existing score-level fusion techniques is made. Finally, conclusions are given in Section V. II. P ROPOSED N ORMALIZATION T ECHNIQUES Matching scores for the modality k in a multi-biometric system is constructed by comparing the features of each individual with that of all the other subjects. This comparison i, j produces raw matching scores denoted by s p,q (k), where p t h t h corresponds to the p sample of the i person, q corresponds to the q t h sample of the j t h person, (i, j = 1, 2, . . . , M), and ( p, q = 1, 2, . . . , N). The genuine score set, G(k), for the k t h modality is obtained as follows: i, j

G(k) = {s p,q (k)} wher e p = q

(1)

The impostor score set, I (k), is obtained as follows i, j

I (k) = {s p,q (k)} wher e i = j

(2)

An example of matching scores for modality k (say, the earprint) of 3 individuals (i.e., i, j = 1, 2, 3) each having 2 samples (i.e., p, q = 1, 2) is given in Table I. The matching score can be represented as a partitioned matrix for this example as follows. ⎡ ⎤ S1,1 S1,2 S1,3 ⎢ ⎥ S2,3 ⎦ S = ⎣ (S1,2 )T S2,2 (3) 1,3 T 2,3 T 3,3 (S ) (S ) S   1,1 2,2 − s − s 1,2 1,2 , S3,3 = where S1,1 = , S2,2 = 1,1 2,2 s2,1 − s2,1 −    3,3 1,2 1,2 1,3 1,3 − s1,2 s1,2 s1,2 s1,1 s1,1 1,2 1,3 , S = = 3,3 1,2 1,2 , S 1,3 1,3 , and s2,1 − s2,1 s2,2 s2,1 s2,2  2,3 2,3 s s1,2 S2,3 = 1,1 2,3 2,3 . The elements of the transpose matrices, s2,1 s2,2 (S1,2 )T , (S1,3 )T , (S2,3 )T are not included in the impostor 1,1 , scores, because of their symmetrical nature. Similarly, s2,1 2,2 3,3 and s2,1 are not included in the genuine scores since s2,1 S1,1 , S2,2 and S3,3 are symmetric. Therefore, the genuine 1,1 2,2 3,3 score set is G(k) = {s1,2 , s1,2 , s1,2 } and the impostor 1,2 1,2 1,2 1,2 1,3 1,3 1,3 1,3 score set is I (k) = {s1,1 , s1,2 , s2,1 , s2,2 , s1,1 , s1,2 , s2,1 , s2,2 , 2,3 2,3 2,3 2,3 s1,1 , s1,2 , s2,1 , s2,2 } for the earprint modality for this example. The proposed normalization and weighting techniques utilize

Fig. 1. Genuine and impostor scores of a biometric system with overlap and non-overlap regions.

the two score sets, G(k) and I (k) in order to improve the recognition rate of multi-biometric systems. The proposed normalization techniques are based on the anchor values and therefore they inherit the advantages of the anchored normalization methods. As discussed in Section I, the anchor value can be computed based on the statistical properties (e.g., EER) of the biometric data or the repeated score set that is obtained from the raw matching scores. In this paper, the anchor values are obtained from the genuine and impostor scores. Therefore, the proposed anchored normalization techniques do not require EER, unlike the case of the PAN-MM normalization method. The proposed anchored normalization techniques also do not require any repeated score set as needed by the IAMM method. In general, there is an overlap region (OLR) between the genuine and impostor scores as shown in Fig. 1. The region of the genuine and impostor scores can be divided into four parts, such as A, B, C and D. The matching scores in parts A and D are clearly impostor and genuine scores (non-overlap scores), respectively. The matching score values in parts B and C are common (overlap scores) between the genuine and impostor scores. It indicates that these scores exist in both the genuine and impostor sets. The recognition errors arise due to this overlap region of the genuine and impostor scores [11]. Therefore, a genuine acceptance rate at a lower false acceptance rate can be achieved by considering the information contained in the overlap scores of the genuine and impostor sets. In view of this, we consider two choices for the anchor values, and refer to them as the overlap extrema based anchor (OEBA) and mean-to-overlap extrema based anchor (MOEBA) values, which are obtained from the genuine and impostor scores. In order to obtain these anchor values, we utilize four parameters of the genuine and impostor scores, namely, min(G(k)), μ(G(k)), max(I (k)) and μ(I (k)), which are the minimum and mean values of genuine scores, and maximum and mean values of the impostor scores, respectively. We assume that a biometric source with high performance produces genuine scores that have a wide mean-to-max range and a wide mean-to-min impostor score range. The general block diagram of the proposed normalization techniques for a bimodal biometric system is shown in Fig. 2. It can be

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 13, NO. 8, AUGUST 2018

respectively, in order to obtain the normalized scores for a given modality as i, j

s¯ p,q (k) ⎧ i, j ⎪ s p,q (k)−mi n{G(k), I (k)} ⎪ i, j ⎪ ⎨ , i f s p,q (k) ≤ An (k) 2(A (k)−mi n{G(k), I (k)} n = i, j ⎪ s p,q (k)− An (k) ⎪ i, j ⎪ , i f s p,q (k) > An (k) ⎩0.5+ max{G(k), I (k)}− An (k) (6)

Fig. 2. (a) Block diagram of a bimodal biometric system using proposed normalization techniques, (b) Normalization block.

seen from this figure that the anchor values are obtained from the genuine and impostor scores, which are then utilized to normalize the individual matching scores. A. Overlap Extrema-Based Anchor (OEBA) In this case, the anchor value is computed from the overlap region of the genuine and impostor scores. A low performance biometric system has a wide overlap area between the genuine and impostor scores. The minimum and maximum values of genuine and impostor scores, respectively, represent the lowest correct score values in their corresponding sets. In order to obtain the anchor value taking the overlap extremas into consideration, we compute the difference between the maximum of the impostor scores and the minimum of the genuine scores. The overlap extrema-based anchor (OEBA) value for modality k, denoted by A1 (k), in a multi-biometric system is formulated as A1 (k) = max(I (k)) − mi n(G(k))

where p = q, if i = j , and n = 1 or 2. The proposed OEBAMM and MOEBAMM are anchorbased normalization techniques. The anchor value for the OEBAMM normalization technique is computed by taking the overlap scores into account. On the other hand, the anchor value for the MOEBAMM normalization technique is computed considering not only the overlap scores but also its scores in the neighboring region in the genuine and impostor segments. III. P ROPOSED W EIGHTING T ECHNIQUE Since we have assumed that a biometric source with high performance produces genuine scores that have a small meanto-max range and a small mean-to-min impostor score range, we define a parameter α(k) as α(k) = {max(G(k)) − μ(G(k))} + {μ(I (k)) − mi n(I (k))} (7) It should be noted that the parameter α(k) emphasizes the nonoverlap scores that are far removed from the overlap region and excludes those that are close to the overlap region. The reason behind this is that the genuine scores that are close to the overlap region have values lower than μ(G(k)) and the impostor scores that are close to the overlap region have values higher than μ(I (k)). The confidence of a matcher for modality k has been defined in [22] as the normalized difference between the best match score and the mean of the t subsequent match scores by

(4)

B. Mean-to-Overlap Extrema-Based Anchor (MOEBA) In this case, we consider the region between the mean of the imposter scores and its maximum value, as well as that between the mean of the genuine scores and its minimum value. The mean-to-overlap extrema-based anchor (MOEBA), denoted by A2 (k), aims to focus on the overlap scores and its neighbors in both the genuine and impostor scores. It is computed by taking the summation of these two regions as follows. A2 (k) = {max(I (k))−μ(I (k))}+{μ(G(k))−mi n(G(k))} (5) The above two anchors, OEBA and MOEBA, are utilized in the anchored min-max techniques, referred to as the OEBAMM and MOEBAMM normalization techniques,

ck =

|sk1 − μk | μk

(8)

where ck is the confidence of the matcher for modality k, sk1 is the best matching score, and μk is the mean of the t subsequent match scores for the modality k. A higher value of ck corresponds to a strong classification, while a smaller value to a weak classification. In our work, (8) is modified based i, j on the raw matching scores (s p,q (k)) and the parameter α(k) in order to compute the confidence of the matching scores for i, j the modality k. We consider the raw matching scores (s p,q (k)) rather than only one score value (namely, the best matching score) to focus on the confidence of the individual scores for a modality. We consider the value of α(k) rather than only μk , since the former emphasizes those scores that are outside the i, j overlap region and its neighbors. Thus, R p,q (k), the confidence of the individual scores, is defined as i, j

i, j

R p,q (k) =

|s p,q (k) − α(k)| α(k)

(9)

KABIR et al.: NORMALIZATION AND WEIGHTING TECHNIQUES BASED ON GENUINE-IMPOSTOR SCORE FUSION

TABLE II C ONFIDENCE OF M ATCHING S CORES OF 3 P ERSONS E ACH H AVING 2 S AMPLES FOR THE E ARPRINT M ODALITY (i, j = 1, 2, 3; p, q = 1, 2)

i, j

where p = q, if i = j . It should be noted that R p,q (k) = j,i i,i Rq, p (k) (if i = j ) and R i,i p,q (k) = Rq, p (k) (if p  = q). An example of the confidence of the matching scores for modality k (i.e., earprint) of 3 individuals (i.e., i, j = 1, 2, 3) each having 2 samples (i.e., p, q = 1, 2) is given in Table II. Now, we compute the confidence of a matcher, β(k) for the modality k as  i, j ∀i, j, p,q R p,q (k) (10) β(k) = z where i = j , and z is the total number of elements  p = q, if i, j in R ∀i, j, p,q p,q (k). A higher value of β(k) corresponds to a strong classification, while a smaller value to a weak classification. It is noted that for the example in Table II z = 30. The confidence of a matcher, β(k) is utilized to compute the weight w(k) assigned for the modality k, and referred to as the confidence-based weighting (CBW) technique. The weight w(k) assigned for the modality k is the normalized value of β(k) and is given by β(k) w(k) = m k=1 β(k)

(11)

where m is the total number of modalities. IV. E XPERIMENTAL R ESULTS In order to show the improvement in the performance of a multi-biometric system using the proposed normalization and weighting techniques introduced in this paper, we consider three modalities, namely, fingerprint, palmprint and earprint. It should be noted that one of the three modalities, namely, the earprint, is from a different part of the human body. A. Databases The images for fingerprint, palmprint and earprint are obtained from four databases, namely, FVC2002-DB1-A fingerprint database [43], COEP and IITD palmprint databases [44]–[46] and AMI earprint database [47], respectively. 1) FVC2002-DB1-A Fingerprint Database: This database was created for Second International Competition for Fingerprint Verification Competition (FVC) Algorithms. It contains fingerprints of 100 subjects and 8 samples per subject. In total, there are 800 gray-level images acquired through an optical sensor of size 388 × 374 and 500 dpi resolution.

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2) COEP Palmprint Database: It contains 168 subjects and 8 samples per individuals in RGB-levels. The images were captured with a digital camera having a resolution of 1600 × 1200. 3) IITD Palmprint Database: It contains palmprint images of right hands of 235 users, with seven images from each subject. The resolution of these palmprint images is 800×600 pixels. 4) AMI Earprint Database: There are 175 RGB-level images in this database having size of 492 × 702 pixels covering 28 subjects. 5) Merged Databases of Fingerprint, Palmprint and Earprint: Since we aim to evaluate the performance of the multi-biometric system that contains fingerprint, palmprint and earprint, it is necessary to have a database that contains scores of these three modalities. But, to the best of our knowledge, there is no such public-domain database that provides the matching scores of fingerprint, palmprint and earprint of the same individual. Therefore, we form two chimeric databases in which the subjects are virtual, and refer these databases as virtual database-1 and virtual database-2. We select 25 subjects and 6 samples per individual from FVC2002-DB1-A, COEP and AMI databases to build virtual database-1. We select 25 subjects and 6 samples per individual from FVC2002DB1-A, IITD and AMI databases to build virtual database-2. Therefore, there are 150 images for each of the modalities in each virtual databases. B. Fusion Rules In this paper, the fused score is obtained using two fusion rules, namely, simple sum (SS) and weighted sum (WS). These rules are discussed below. 1) Simple Sum (SS) Rule-Based Fusion: The simple-sum (SS) fusion rule is utilized to combine the normalized scores obtained using the proposed normalization techniques. This fusion rule does not assign any weights for the individual matchers in the system. After we get the normalized scores for the k (k = 1, 2, . . . , m) modalities using the proposed i, j techniques, the fused score, f p,q , is evaluated using i, j

f p,q =

m 

i, j

s¯ p,q (k)

(12)

k=1 i, j

where s¯ p,q (k) are the normalized scores for the modality k. 2) Weighted Sum (WS) Rule-Based Fusion: The weighted sum fusion rule is utilized to combine the normalized scores along with the estimated weights for the individual matchers obtained using the proposed normalization and weighting techniques. After we get the normalized scores and the estimated weights for the k (k = 1, 2, . . . , m) modalities using the i, j proposed techniques, the fused score, f p,q , is evaluated using i, j

f p,q =

m 

i, j

w(k)¯s p,q (k)

(13)

k=1 i, j

where s¯ p,q (k) are the normalized scores for the modality k.

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TABLE III EER(%) AND GAR @0.5% FAR P ROVIDED BY I NDIVIDUAL B IOMETRIC S YSTEMS AND THE M ULTI -B IOMETRIC S YSTEM W ITH NO N ORMALIZATION AND NO W EIGHTING (NN-NW) FOR THE V IRTUAL D ATABASES . (FP-F INGERPRINT, PP-PALMPRINT, EP-E ARPRINT )

C. Performance Results Matching scores are generated for the three modalities using the techniques in [48] and [49]. In a biometric system, false acceptance rate (FAR) is the ratio of the number of false subjects accepted for a predefined threshold to the total number of enrolled subjects, while the genuine acceptance rate (GAR) is the ratio of the number of genuine subjects accepted for a predefined threshold to the total number of enrolled subjects [20]. False rejection rate (FRR) is the ratio of the number of genuine subjects rejected for a predefined threshold to the total number of enrolled subjects [20]. The lower the value of EER and higher the value of GAR at a lower FAR, the better is the biometric system. The performance of a multi-biometric system is measured in terms of EER and GAR @0.5% FAR. Receiver operating characteristics (ROCs) are generated in terms of the genuine acceptance rate as a function of the false acceptance rate. In order to study the performance of the proposed techniques for normalization and weighting, and compare it with existing techniques for normalization and weighting, we perform the following experiments. In Experiment 1, we study the performance of the multibiometric system using the simple sum fusion rule without any normalization for the virtual databases. Table III gives EER(%) and GAR @0.5% FAR provided by the individual biometric systems as well as by the multi-biometric system without normalization for the virtual databases. This table shows that the performance of the multi-biometric system without normalization using the simple sum fusion rule is better than that of the earprint (EP) system and comparable to that of the palmprint (PP) system, but inferior to that of the fingerprint (FP) system. In other words, as expected, a unimodal biometric system, such as the FP system in the present case, is a better choice than a multi-biometric system without normalization using the simple sum rule, since the overall performance of a multi-biometric system in terms of GAR may be lower than that of the unimodal system due to the lowest GAR provided by the weakest matcher. The above statements are true for both virtual databases. In Experiment 2, we study the performance of the multibiometric system using the proposed weighting technique (CBW), but with no normalization. Table IV depicts EER(%) and GAR @0.5% FAR provided by the multi-biometric system using the proposed and existing weighting techniques under the weighted sum fusion rule for the virtual databases on the raw matching scores. The table shows that the performance

TABLE IV EER(%) AND GAR @0.5% FAR P ROVIDED BY THE M ULTI -B IOMETRIC S YSTEM U SING THE P ROPOSED AND E XISTING W EIGHTING T ECHNIQUES U NDER THE W EIGHTED S UM F USION RULE FOR THE V IRTUAL D ATABASES ON THE R AW M ATCHING S CORES

of the multi-biometric system using the proposed weighting technique is superior to that of the system using existing weighting techniques, namely, DPW, FDRW and SRBW. A comparison of the results given in Tables III and IV shows that there is no advantage to be gained by using a multi-biometric system by simply employing a weighting technique (without normalization) over using a uni-modal system that has the best performance (namely, FP in the present case). The above statements are true for both virtual databases. In Experiment 3, we study the performance of the multi-biometric system using the proposed normalization techniques (namely, OEBAMM and MOEBAMM). Table V shows EER(%) and GAR @0.5% FAR provided by the multibiometric system using the proposed and existing normalization techniques under the simple sum fusion rule for the virtual databases. This table shows that the performance of the multibiometric system using MOEBAMM is better than that of the system using OEBAMM and the existing normalization techniques in terms of GAR @0.5% FAR. It also shows that the multi-biometric system using MOEBAMM provides the second lowest EER, still comparable to the lowest EER. A comparison of the results given in Tables III and V shows that the multi-biometric system using any normalization technique provides a higher value of GAR @0.5% FAR than that provided by any of the unimodal systems for virutal database-1. This comparison also shows that the multi-biometric system using any normalization technique except MM and PAN-MM provides a value of GAR @0.5% FAR higher than that provided by any of the unimodal systems for virtual database-2.

KABIR et al.: NORMALIZATION AND WEIGHTING TECHNIQUES BASED ON GENUINE-IMPOSTOR SCORE FUSION

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TABLE V EER(%) AND GAR @0.5% FAR P ROVIDED BY THE M ULTI -B IOMETRIC S YSTEM U SING THE P ROPOSED AND E XISTING N ORMALIZATION T ECHNIQUES U NDER THE S IMPLE S UM F USION RULE FOR THE V IRTUAL D ATABASES

TABLE VI EER(%) AND GAR @0.5% FAR P ROVIDED BY THE M ULTI -B IOMETRIC S YSTEM U SING VARIOUS N ORMALIZATION AND W EIGHTING T ECHNIQUES U NDER THE W EIGHTED S UM F USION RULE FOR THE V IRTUAL D ATABASES

In addition, the multi-biometric system using any of the normalization techniques except PAN-MM provides a value of EER lower than that provided by the unimodal systems for both the virtual databases. In Experiment 4, we study the performance of the multibiometric system using the various normalization and weighting techniques. Table VI shows EER(%) and GAR @0.5% FAR provided by the multi-biometric system under the weighted sum fusion rule for the virtual databases. In this table, individual columns correspond to EERs and GARs @0.5% FARs provided by the multi-biometric system using a given normalization technique and various weighting techniques including the proposed one. In this table, the lowest EER and the highest GAR @0.5% FAR are indicated with bold typeface. By comparing the results given in Tables III and VI, we see that the multi-biometric system using any technique

for normalization and any weighting technique provides a lower value of EER and a higher value of GAR @0.5% FAR than that provided by any of the unimodal systems. For virtual database-1, there are five cases where the system provides the lowest EER value of 0.54% and highest GAR @0.5% FAR value of 99.47%. In all these cases, the lowest EER and the highest GAR @0.5% FAR are provided by the multi-biometric system that utilizes either one of the proposed normalization techniques or the proposed weighting technique. Between the proposed normalization techniques, OEBAMM is a better choice than MOEBAMM in terms of computational cost, since the former does not require computing the mean values of the genuine and impostor scores. OEBAMM is also better than MM in terms of computational cost, in view of the fact that the former does not require the maximum and minimum values of the raw matching scores to be computed.

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Fig. 3. Performance comparison of the individual biometric systems and the multi-biometric system using OEBAMM and MOEBAMM normalization techniques under the simple sum fusion rule for virtual database-1.

Fig. 4. Performance comparison of the multi-biometric system using the proposed and existing normalization techniques under the simple sum fusion rule for virtual database-1.

As for the weighting techniques, the proposed one is better than DPW in terms of the computational cost, since the former does not require the standard deviation of the genuine and impostor scores to be computed. Based on all these, it can be stated that the proposed OEBAMM normalization along with the proposed weighting technique is the best choice in order to achieve the lowest EER and highest GAR @0.5% FAR with the lowest computational cost for the multi-biometric system. For virtual database-2, the system provides the lowest EER value of 1.12% and highest GAR @0.5% FAR value of 97.33% using the proposed OEBAMM normalization and CBW weighting techniques. Fig. 3 shows ROC curves of the individual biometric systems and the multi-biometric system using the proposed OEBAMM and MOEBAMM normalization techniques under the simple sum fusion rule for the virtual database-1. It can be seen from this figure that the system using OEBAMM and MOEBAMM normalization techniques provides a higher GAR @0.5% FAR than that provided by any of the uni-biometric systems.

Fig. 4 shows ROC curves of the multi-biometric system using the proposed and existing normalization techniques for the virtual database-1. As can be seen from this figure, the system using OEBAMM normalization technique provides a higher value of GAR @0.5% FAR than that provided by the system using the existing normalization methods except for IAMM. However, the system using IAMM has a higher computational cost than that using OEBAMM, since the former requires computing the standard deviation of the matching scores. It can also be observed that the system using MOEBAMM provides the highest GAR @0.5% FAR. Figs. 5-9 show the ROC curves of the system using a given normalization technique with various weighting methods for the virtual database-1. As can be seen from these figures, the performance of the system using MM, PAN-MM, OEBAMM and MOEBAMM normalization techniques with the proposed weighting technique is superior to that of the system using existing weighting techniques, while the performance of the system

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Fig. 5. Effects of MM normalization method on the performance of the multi-biometric system using the proposed and existing weighting techniques under the weighted sum fusion rule for virtual database-1.

Fig. 6. Effects of PAN-MM normalization method on the performance of the multi-biometric system using the proposed and existing weighting techniques under the weighted sum fusion rule for virtual database-1.

Fig. 7. Effects of IAMM normalization method on the performance of the multi-biometric system using the proposed and existing weighting techniques under the weighted sum fusion rule for virtual database-1.

using IAMM with the proposed weighting technique is comparable to that of the system using existing weighting techniques. Figs. 10 shows the ROC curve of the system using OEBAMM normalization technique with the various

weighting methods for virtual database-2. It is seen from this figure that the the performance of the system using OEBAMM normalization technique with the proposed weighting technique is superior to that of the system using the existing weighting techniques.

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Fig. 8. Effects of OEBAMM normalization method on the performance of the multi-biometric system using the proposed and existing weighting techniques under the weighted sum fusion rule for virtual database-1.

Fig. 9. Effects of MOEBAMM normalization method on the performance of the multi-biometric system using the proposed and existing weighting techniques under the weighted sum fusion rule for virtual database-1.

Fig. 10. Effects of OEBAMM normalization method on the performance of the multi-biometric system using the proposed and existing weighting techniques under the weighted sum fusion rule for virtual database-2.

V. C ONCLUSION In this paper, we have presented two new normalization techniques and a new weighting method at score-level fusion for multi-biometric systems. The first normalization

technique, referred to as overlap extrema based anchored min-max (OEBAMM) normalization technique, uses the overlap region (OLR) between the genuine and impostor scores. The second normalization technique, referred to as

KABIR et al.: NORMALIZATION AND WEIGHTING TECHNIQUES BASED ON GENUINE-IMPOSTOR SCORE FUSION

mean-to-overlap extrema based anchored min-max (MOEBAMM) normalization technique, utilizes the OLR and its neighbors by considering the means of the genuine and impostor scores. The proposed weighting technique, referred to as confidence-based weighting (CBW) technique, depends on the region between the mean of the genuine scores and its maximum value, as well as the region between the mean of the impostor scores and its minimum value. The performance of the proposed techniques has been evaluated on a multi-biometric system having three traits, namely, fingerprint, palmprint, and earprint. The experimental results show that the performance of the multi-biometric system using the proposed normalization and weighting techniques is superior to that provided by any of the unimodal systems. Further, results show that the multi-biometric system provides the lowest EER and highest GAR @0.5% FAR only when one of the two proposed normalization techniques or the proposed weighting technique is employed. However, if the computational cost is also taken into the consideration, then the best choice is to use the proposed OEBAMM technique for normalization and the proposed confidence-based weighting technique. The scope of this paper has been to develop new normalization and weighting techniques when the fusion of the individual biometrics is carried out at the score-level. The proposed confidence-based weighting (CBW) technique can be adapted to the fusions at other levels provided that the information on genuine, impostor and matching scores are available. We intend to undertake this investigation in a future study. R EFERENCES [1] A. Jain, P. Flynn, and A. Ross, Handbook of Biometrics. New York, NY, USA: Springer-Verlag, 2007. [2] A. Ross, K. Nandakumar, and A. Jain, Handbook of Multibiometrics. New York, NY, USA: Springer, 2011. [3] Y. J. Chin, T. S. Ong, A. B. J. Teoh, and K. O. M. Goh, “Integrated biometrics template protection technique based on fingerprint and palmprint feature-level fusion,” Inf. Fusion, vol. 18, pp. 161–174, Jul. 2014. [4] A. Nagar, K. Nandakumar, and A. K. Jain, “Multibiometric cryptosystems based on feature-level fusion,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 1, pp. 255–268, Feb. 2012. [5] X.-Y. Jing, Y.-F. Yao, D. Zhang, J.-Y. Yang, and M. Li, “Face and palmprint pixel level fusion and kernel DCV-RBF classifier for small sample biometric recognition,” Pattern Recognit., vol. 40, no. 3, pp. 3209–3224, Nov. 2007. [6] Y.-F. Yao, X.-Y. Jing, and H.-S. Wong, “Face and palmprint feature level fusion for single sample biometrics recognition,” Neurocomputing, vol. 70, nos. 7–9, pp. 1582–1586, Mar. 2007. [7] R. Raghavendra, B. Dorizzi, A. Rao, and G. H. Kumar, “Designing efficient fusion schemes for multimodal biometric systems using face and palmprint,” Pattern Recognit., vol. 44, no. 5, pp. 1076–1088, May 2011. [8] W. Yang, X. Huang, F. Zhou, and Q. Liao, “Comparative competitive coding for personal identification by using finger vein and finger dorsal texture fusion,” Inf. Sci., vol. 268, no. 6, pp. 20–32, 2014. [9] L. Mezai and F. Hachouf, “Score-level fusion of face and voice using particle swarm optimization and belief functions,” IEEE Trans. Human— Mach. Syst., vol. 45, no. 6, pp. 761–772, Dec. 2015. [10] K. Nguyen, S. Denman, S. Sridharan, and C. Fookes, “Score-level multibiometric fusion based on Dempster–Shafer theory incorporating uncertainty factors,” IEEE Trans. Human–Mach. Syst., vol. 45, no. 1, pp. 132–140, Feb. 2015. [11] R. Snelick, U. Uludag, A. Mink, M. Indovina, and A. K. Jain, “Largescale evaluation of multimodal biometric authentication using state-ofthe-art systems,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 450–455, Mar. 2005.

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[35] P. Perakis, T. Theoharis, and I. A. Kakadiaris, “Feature fusion for facial landmark detection,” Pattern Recognit., vol. 47, no. 9, pp. 2783–2793, Sep. 2014. [36] J. Zhou, S. Cadavid, and M. Abdel-Mottaleb, “An efficient 3-D ear recognition system employing local and holistic features,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 3, pp. 978–991, Jun. 2012. [37] N. Werghi, C. Tortorici, S. Berretti, and A. D. Bimbo, “Boosting 3D LBP-based face recognition by fusing shape and texture descriptors on the mesh,” IEEE Trans. Inf. Forensics Security, vol. 11, no. 5, pp. 964–979, May 2016. [38] M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi, “Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition,” IEEE Trans. Inf. Forensics Security, vol. 11, no. 9, pp. 1984–1996, Sep. 2016. [39] H. S. Bhatt, R. Singh, and M. Vatsa, “On recognizing faces in videos using clustering-based re-ranking and fusion,” IEEE Trans. Inf. Forensics Security, vol. 9, no. 7, pp. 1056–1068, Jul. 2014. [40] R. Nishii, “A Markov random field-based approach to decision-level fusion for remote sensing image classification,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 10, pp. 2316–2319, Oct. 2003. [41] Q. Tao and R. Veldhuis, “Threshold-optimized decision-level fusion and its application to biometrics,” Pattern Recognit., vol. 42, no. 5, pp. 823–836, May 2009. [42] A. C. Lorena and A. C. P. L. F. de Carvalho, “Building binary-tree-based multiclass classifiers using separability measures,” Neurocomputing, vol. 73, nos. 16–18, pp. 2837–2845, Oct. 2010. [43] FVC2002 The 2nd Fingerprint Verification Competition. Accessed: Jun. 2017. [Online]. Available: http://bias.csr.unibo.it/fvc2002/ [44] COEP Palm Print Database (College of Engineering Pune-411005 (An Autonomous Institute of Government of Maharashtra). Accessed: Jun. 2017. [Online]. Available: http://www.coep.org.in/ [45] IIT Delhi Palmprint Image Database Version 1.0. Accessed: Jun. 2017. [Online]. Available: http://www4.comp.polyu.edu.hk/ csajaykr/IITD/Database_Palm.htm [46] A. Kumar, “Incorporating cohort information for reliable palmprint authentication,” in Proc. 6th Indian Conf. Comput. Vis., Graph. Image Process. (ICVGIP), Bhubneshwar, India, Dec. 2008, pp. 583–590. [47] AMI Ear Database. Accessed: Jun. 2017. [Online]. Available: http://www.ctim.es/research_works/ami_ear_database/ [48] J. Abraham, P. Kwan, and J. Gao, “Fingerprint matching using a hybrid shape and orientation descriptor,” in State of the Art in Biometrics. Rijeka, Croatia: InTech, 2011, pp. 25–56. [49] Z. Khan, A. Mian, and Y. Hu, “Contour code: Robust and efficient multispectral palmprint encoding for human recognition,” in Proc. ICCV, 2011, pp. 1935–1942. Waziha Kabir received the B.Sc. degree in electrical, electronics and communication engineering from the University of Dhaka, Bangladesh, in 2007, and the M.A.Sc. degree in electrical and computer engineering from Concordia University, Montreal, QC, Canada, in 2013. She is currently pursuing the Ph.D. degree in electrical and computer engineering with Concordia University, Montreal, QC, Canada. She was a full-time Lecturer with the Department of Electrical, Electronics and Communication Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh, from 2008 to 2011. Her research interests include biometrics, image processing, and computer vision. She is a member of the IEEE Biometrics Council, and Circuits and Systems Society. She was a recipient of numerous honors and awards, including the Concordia University Tuition Fee Remission Award and the MIST Medal from the Military Institute of Science and Technology. She has served as a reviewer for several IEEE journals and major conferences. M. Omair Ahmad (F’13) received the B.Eng. degree in electrical engineering from Sir George Williams University, Montreal, QC, Canada, and the Ph.D. degree in electrical engineering from Concordia University, Montreal, QC, Canada. From 1978 to 1979, he was a Faculty Member with the New York University College, Buffalo, NY, USA. In 1979, he joined the Faculty of Concordia University as an Assistant Professor of computer science. He joined the Department of Electrical and Computer Engineering, Concordia University, where he was Chair of the department from 2002 to 2005 and is currently a Professor. He is

the Concordia University Research Chair (Tier I) in Multimedia Signal Processing. He has published extensively in the area of signal processing and holds four patents. His current research interests include the areas of multidimensional filter design, speech, image and video processing, nonlinear signal processing, communication DSP, artificial neural networks, and VLSI circuits for signal processing. He was a Founding Researcher with Micronet from its inception in 1990 as a Canadian Network of Centers of Excellence until its expiration in 2004. He was an Examiner of the order of Engineers of Quebec. In 1988, he was a member of the Admission and Advancement Committee of the IEEE. He has served as the Program Co-Chair for the 1995 IEEE International Conference on Neural Networks and Signal Processing, the 2003 IEEE International Conference on Neural Networks and Signal Processing, and the 2004 IEEE International Midwest Symposium on Circuits and Systems. He was the Local Arrangements Chairman of the 1984 IEEE International Symposium on Circuits and Systems. He was a recipient of numerous honors and awards, including the Wighton Fellowship from the Sandford Fleming Foundation, an induction to Provosts Circle of Distinction for Career Achievements, and the Award of Excellence in Doctoral Supervision from the Faculty of Engineering and Computer Science, Concordia University. He was the General Co-Chair for the 2008 IEEE International Conference on Neural Networks and Signal Processing. He is the Chair of the Montreal Chapter IEEE Circuits and Systems Society. He was an Associate Editor of the IEEE T RANSACTIONS ON C IRCUITS AND S YSTEMS PART I: F UNDAMENTAL T HEORY AND A PPLICATIONS from 1999 to 2001.

M. N. S. Swamy (S’59–M’62–SM’74–F’80) received the B.Sc. degree (Hons.) in mathematics from Mysore University, India, in 1954, the Diploma degree in electrical communication engineering from the Indian Institute of Science, Bangalore, in 1957, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Saskatchewan, Saskatoon, SK, Canada, in 1960 and 1963, respectively. He is currently a Research Professor and the Concordia Chair (Tier I) in signal processing with the Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada, where he served as the Founding Chair with the Department of Electrical Engineering from 1970 to 1977, and the Dean of Engineering and Computer Science from 1977 to 1993. During that time, he developed the faculty into a research-oriented one from what was primarily an undergraduate faculty. He has also taught in the Electrical Engineering Department, Technical University of Nova Scotia, Halifax, and the University of Calgary, Calgary, and also with the Department of Mathematics, University of Saskatchewan. He has published extensively in the areas of number theory, circuits, systems, and signal processing, and holds five patents. He is a co-author of nine books and several book chapters. He was a Founding Member of Micronet from its inception in 1990 as a Canadian Network of Centers of Excellence until its expiration in 2004 and also the Coordinator for Concordia University. He is a fellow of the Institute of Electrical Engineers, U.K., the Engineering Institute of Canada, the Institution of Engineers, India, and the Institution of Electronic and Telecommunication Engineers, India. He was inducted in 2009 to the Provosts Circle of Distinction for career achievements. He has served the IEEE in various capacities, such as the President-Elect in 2003, President in 2004, Past-President in 2005, Vice President (Publications) from 2001 to 2002, and Vice-President in 1976. He was conferred in 2009 the title of Honorary Professor at National Chiao Tong University, Taiwan. He was a recipient of many IEEE-CAS Society awards, including the Education Award in 2000, the Golden Jubilee Medal in 2000, and the 1986 Guillemin-Cauer Best Paper Award. He served as the Program Chair for the 1973 IEEE CAS Symposium, the General Chair for the 1984 IEEE CAS Symposium, the Vice-Chair for the 1999 IEEE Circuits and Systems (CAS) Symposium. He served as Editor-in-Chief of the IEEE T RANSACTIONS ON C IRCUITS AND S YSTEMS I from 1999 to 2001, and an Associate Editor of the IEEE T RANSACTIONS ON C IRCUITS AND S YSTEMS from 1985 to 1987. He has been the Editor-in-Chief of the journal Circuits, Systems and Signal Processing (CSSP) since 1999. Recently, the CSSP has instituted the Best Paper Award in his name.