Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`Integrating Faces, Fingerprints, and Soft Biometric
`Traits for User Recognition
`
`Anil K. Jain, Karthik Nandakumar, Xiaoguang Lu, and Unsang Park
`
`Department of Computer Science and Engineering
`{jain,nandakum,lvxiaogu,parkunsa}@cse.msu.edu
`Michigan State University, MI - 48824, USA
`
`Abstract. Soft biometric traits like gender, age, height, weight, ethnicity, and
`eye color cannot provide reliable user recognition because they are not distinc-
`tive and permanent. However, such ancillary information can complement the
`identity information provided by the primary biometric traits (face, fingerprint,
`hand-geometry, iris, etc.). This paper describes a hybrid biometric system that
`uses face and fingerprint as the primary characteristics and gender, ethnicity, and
`height as the soft characteristics. We have studied the effect of the soft biometric
`traits on the recognition performance of unimodal face and fingerprint recognition
`systems and a multimodal system that uses both the primary traits. Experiments
`conducted on a database of 263 users show that the recognition performance of
`the primary biometric system can be improved significantly by making use of
`soft biometric information. The results also indicate that such a performance im-
`provement can be achieved only if the soft biometric traits are complementary to
`the primary biometric traits.
`
`1 Introduction
`
`Biometric systems recognize users based on their physiological and behavioral charac-
`teristics [1]. Unimodal biometric systems make use of a single biometric trait for user
`recognition. It is difficult to achieve very high recognition rates using unimodal systems
`due to problems like noisy sensor data and non-universality and/or lack of distinctive-
`ness of the chosen biometric trait. Multimodal biometric systems address some of these
`problems by combining evidence obtained from multiple sources [2]. A multimodal
`biometric system that utilizes a number of different biometric identifiers like face, fin-
`gerprint, hand-geometry, and iris can be more robust to noise and alleviate the prob-
`lem of non-universality and lack of distinctiveness. Hence, such a system can achieve
`a higher recognition accuracy than unimodal systems. However, a multimodal system
`will require a longer verification time thereby causing inconvenience to the users.
`It is possible to improve the recognition performance of a biometric system with-
`out compromising on user-friendliness by utilizing ancillary information about the user
`like height, weight, age, gender, ethnicity, and eye color. We refer to these traits as
`soft biometric traits because they provide some information about the individual, but
`lack the distinctiveness and permanence to sufficiently differentiate any two individuals
`(see Figure 1 for examples of soft biometric traits). The soft biometric traits can either
`be continuous or discrete. Traits such as gender, eye color, and ethnicity are discrete
`
`1
`
`Reactive Surfaces Ltd. LLP
`Ex. 1035 (Rozzell Attachment P)
`Reactive Surfaces Ltd. LLP v. Toyota Motor Corp.
`IPR2016-01914
`
`

`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`in nature. On the other hand, traits like height and weight are continuous variables.
`Heckathorn et al. [3] have shown that a combination of soft attributes like gender, race,
`eye color, height, and other visible marks like scars and tattoos can be used to identify
`an individual only with a limited accuracy. Hence, the ancillary information by itself
`is not sufficient to recognize a user. However, soft biometric traits can complement the
`traditional (primary) biometric identifiers like fingerprint and hand-geometry and hence
`improve the performance of the primary biometric system.
`
`Fig. 1. Examples of soft biometric traits.
`
`In order to utilize soft biometrics, there must be a mechanism to automatically ex-
`tract these features from the user during the recognition phase. As the user interacts
`with the primary biometric system, the system should be able to automatically extract
`the soft biometric characteristics like height, weight, age, gender, and ethnicity in a non-
`obtrusive manner without any interaction with the user. In section 2 we present some
`of the methods that could be used for automatic extraction of the soft biometric infor-
`mation. Section 3 describes our framework for the integration of soft biometrics with
`the primary biometric system. The objective of this work is to analyze the impact of
`
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`

`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`introducing soft biometric variables like gender, ethnicity, and height into the decision
`making process of a recognition system that uses faces and fingerprints as the primary
`biometric traits. The experimental results presented in section 4 give an insight on the
`effects of different soft biometric variables on the recognition performance.
`
`2 Automatic Extraction of Soft Biometric Characteristics
`
`Soft biometric characteristics like gender, ethnicity, and age could be derived from the
`facial image of the user. Several studies have attempted to identify the gender, ethnicity,
`and pose of the users from their facial images. Gutta et al. [4] proposed a mixture of
`experts consisting of ensembles of radial basis functions for the classification of gen-
`der, ethnic origin, and pose of human faces. They also used a SVM classifier with RBF
`kernel for gating the inputs. Their gender classifier classified users as either male or
`female with an average accuracy rate of 96%, while their ethnicity classifier classified
`users into Caucasian, South Asian, East Asian, and African with an accuracy of 92%.
`These results were reported on good quality face images from the FERET database that
`had very little expression or pose changes. Based on the same database, Moghaddam
`and Yang [5] showed that the error rate for gender classification can be reduced to 3.4%
`by using an appearance-based gender classifier that uses non-linear support vector ma-
`chines. Shakhnarovich et al. [6] developed a demographic classification scheme that
`extracts faces from unconstrained video sequences and classifies them based on gender
`and ethnicity. Their demographic classifier was a Perceptron constructed from binary
`rectangle features. The learning and feature selection modules used a variant of the Ad-
`aBoost algorithm. Their ethnicity classifier classified users as either Asian or non-Asian.
`Even under unconstrained environments, they showed that a classification accuracy of
`more than 75% can be achieved for both gender and ethnicity classification. For this
`data, the SVM classifier of Moghaddam and Yang had an error rate of 24.5% and there
`was also a notable bias towards males in the classification (females had an error rate of
`28%). Balci and Atalay [7] reported a classification accuracy of more than 86% for a
`gender classifier that uses PCA for feature extraction and Multi-Layer Perceptron for
`classification. Jain and Lu [8] proposed a Linear Discriminant Analysis (LDA) based
`scheme to address the problem of ethnicity identification from facial images. The users
`were identified as either Asian or non-Asian by applying multiscale analysis to the input
`facial images. An ensemble framework based on the product rule was used for integrat-
`ing the LDA analysis at different scales. This scheme had an accuracy of 96.3% on a
`database of 263 users (with approximately equal number of users from the two classes).
`Automatic age determination is a more difficult problem due to the very limited
`physiological or behavioral changes in the human body as the person grows from one
`age group to another. There are currently no reliable biometric indicators for age deter-
`mination [9]. Buchanan et al. [10] have been studying the differences in the chemical
`composition of child and adult fingerprints that could be used to distinguish children
`from adults. Kwon and Lobo [11] present an algorithm for age classification from facial
`images based on cranio-facial changes in feature-position ratios and skin wrinkle anal-
`ysis. They attempted to classify users as “babies”, “young adults”, or “senior adults”.
`However, they do not provide any accuracy estimates for their classification scheme.
`
`3
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`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`One can hope that age determination systems providing a reasonable estimate of the
`age of a person would be available in the near future.
`The weight of a user can be measured by installing a weight sensor at the place
`where the users stand while providing the primary biometric. The height can be es-
`timated from a sequence of real-time images obtained when the user moves into the
`view of the camera. Figure 2 describes a mechanism for simultaneous extraction of the
`height information and the facial image of a user. In this setup we assume that the posi-
`tion of the camera and the background scene are fixed. The background image (Figure
`2(a)) is initially stored in the system. Two markers are placed in the background for
`calibration. The first marker is placed at a height Hlow above the ground and the sec-
`ond marker is placed at a distance Href above the first marker. The vertical distance
`between the two markers in the background image is measured as Dref . In our experi-
`ments, Hlow = 150 cm, Href = 30 cm,and Dref = 67 pixels. The background image
`is subtracted from the current frame (Figure 2(b)) to obtain the difference image (Figure
`2(c)). A threshold is applied to the difference image to detect only those pixels having
`large intensity changes. Median filtering is applied to remove the salt and pepper noise
`in the difference image. The background subtraction is usually performed in color do-
`main [12]. However, for the sake of simplicity in deciding the threshold value and in the
`median filtering operation, we performed the subtraction in the gray-scale domain. The
`difference image is scanned from the top to detect the top of the head and the vertical
`distance between the top of the head and the lowermost marker is measured as Duser
`(in pixels). An estimate of the true height of the person (Huser in cm) is computed as:
`
`Huser = Hlow + Duser
`Dref
`
`Href .
`
`(1)
`
`After the estimation of the height, the face of the user is detected in the captured
`frame using the algorithm proposed by Hsu et al. [13]. After the detection of the facial
`region in the frame (Figure 2(d)), the face is cropped out of the frame and is used by the
`face recognition and gender/ethnicity extraction modules. Since, we have not collected
`sufficient data using this extraction process, we used an off-line face database in our
`experiments.
`
`3 Framework for Integration of Soft Biometrics
`
`We use the same framework proposed in [14] for integrating the soft biometric infor-
`mation with the primary biometric system. In this framework, the biometric recogni-
`tion system is divided into two subsystems. One subsystem is called the primary bio-
`metric system and it is based on traditional biometric identifiers like fingerprint, face
`and hand-geometry. The primary biometric system could be either unimodal or multi-
`modal. The second subsystem, referred to as the secondary biometric system, is based
`on soft biometric traits like age, gender, and height. Figure 3 shows the architecture of
`a personal identification system that makes use of fingerprint, face and soft biometric
`measurements. Let ω1, ω2,··· , ωn represent the n users enrolled in the database. Let
`
`4
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`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`(a)
`
`(c)
`
`(b)
`
`(d)
`
`Fig. 2. Extraction of height and facial image from the user (a) background image (b) Current
`frame (c) Difference Image (d) Location of the face in the current frame.
`
`x be the feature vector corresponding to the primary biometric. Without loss of gen-
`erality, let us assume that the output of the primary biometric system is of the form
`P (ωi | x), i = 1, 2,··· , n, where P (ωi | x) is the probability that the test user is ωi
`given the feature vector x. If the output of the primary biometric system is a matching
`score, it is converted into posteriori probability using an appropriate transformation. For
`the secondary biometric system, we can consider P (ωi | x) as the prior probability of
`the test user being user ωi.
`
`··· , ym] be the soft biometric feature
`··· , yk, yk+1, yk+2,
`Let y = [y1, y2,
`vector, where y1 through yk are continuous variables and yk+1 through ym are discrete
`variables. The updated probability of user ωi, given the primary biometric feature vector
`x and the soft biometric feature vector y i.e., P (ωi | x, y) can be calculated using the
`Bayes’ rule.
`
`P (ωi|x, y) =
`
`(cid:80)n
`p(y|ωi) P (ωi|x)
`i=1 p(y|ωi) P (ωi|x)
`
`.
`
`(2)
`
`5
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`

`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`Fig. 3. Integration of Soft Biometric Traits with a Primary Biometric System
`(x is the fingerprint feature vector, y is the soft biometric feature vector).
`
`If we assume that the soft biometric variables are independent, equation (2) can be
`rewritten as
`
`. (
`
`P (ωi|x, y) =
`
`(cid:80)n
`p(y1|ωi) ··· p(yk|ωi) P (yk+1|ωi) ··· P (ym|ωi) P (ωi|x)
`i=1 p(y1|ωi) ··· p(yk|ωi) P (yk+1|ωi) ··· P (ym|ωi) P (ωi|x)
`
`3)
`In equation (3), p(yj|ωi), j = 1, 2,··· , k is evaluated from the conditional density
`of the variable yj for user ωi. On the other hand, discrete probability P (yj|ωi), j =
`k + 1, k + 2,··· , m represents the probability that user ωi is assigned to the class yj.
`This is a measure of the accuracy of the classification module in assigning user ωi to one
`of the distinct classes based on biometric indicator yj. In order to simplify the problem,
`let us assume that the classification module performs equally well on all the users and
`therefore the accuracy of the module is independent of the user.
`Let
`
`n(cid:88)
`
`p(y) =
`
`p(y1|ωi) ··· p(yk|ωi) P (yk+1|ωi) ··· P (ym|ωi) P (ωi|x) .
`
`i=1
`
`The logarithm of P (ωi|x, y) in equation (3) can be expressed as
`log P (ωi|x, y) = log p(y1|ωi) + ··· + log p(yk|ωi) + log P (yk+1|ωi) + ···
`+ log P (ym|ωi) + log P (ωi|x) − log p(y) .
`
`(4)
`
`This formulation has two main drawbacks. The first problem is that all the m soft
`biometric variables have been weighed equally. In practice, some variables may con-
`tain more information than the others. For example, the gender of a person may give
`more information about a person than height. Therefore, we must introduce a weighting
`
`6
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`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`scheme for the soft biometric traits based on an index of distinctiveness and perma-
`nence; i.e., traits that have smaller variability and larger distinguishing capability will
`be given more weight in the computation of the final matching probabilities. Another
`potential pitfall is that any impostor can easily spoof the system because the soft char-
`acteristics have an equal say in the decision as the primary biometric trait. It is relatively
`easy to modify/hide one’s soft biometric attributes by applying cosmetics and wearing
`other accessories (like mask, shoes with high heels, etc.). To avoid this problem, we
`assign smaller weights to the soft biometric traits compared to those assigned to the pri-
`mary biometric traits. This differential weighting also has another implicit advantage.
`Even if a soft biometric trait of a user is measured incorrectly (e.g., a male user is iden-
`tified as a female), there is only a small reduction in that user’s posteriori probability
`and the user is not immediately rejected. In this case, if the primary biometric produces
`a good match, the user may still be accepted. Only if several soft biometric traits do
`not match, there is significant reduction in the posteriori probability and the user could
`be possibly rejected. If the devices that measure the soft biometric traits are reasonably
`accurate, such a situation has a low probability of occurrence. The introduction of the
`weighting scheme results in the following discriminant function for user ωi:
`gi(x, y) = a0 log P (ωi|x) + a1 log p(y1|ωi) + ··· + ak log p(yk|ωi) +
`ak+1 log P (yk+1|ωi) + ··· + am log P (ym|ωi),
`
`(cid:80)m
`i=0 ai = 1 and a0 >> ai, i = 1, 2,··· , m. Note that ai’s, i = 1, 2,··· , m
`where
`are the weights assigned to the soft biometric traits and a0 is the weight assigned to the
`primary biometric identifier. It must be noted that the weights ai, i = 1, 2,··· , m must
`be made small to prevent the domination of the primary biometric by the soft biometric
`traits. On the other hand, they must large enough so that the information content of the
`soft biometric traits is not lost. Hence, an optimum weighting scheme is required to
`maximize the performance gain.
`
`(5)
`
`4 Experimental Results
`
`Our experiments demonstrate the benefits of utilizing the gender, ethnicity, and height
`information of the user in addition to the face and fingerprint biometric identifiers. The
`face database described in [8] has been used in our experiments. This database has face
`images of 263 users, with 10 images per user. Our fingerprint database consisted of
`impressions of 160 users obtained using a Veridicom sensor. Each user provided four
`impressions of each of the four fingers, namely, the left index finger, the left middle
`finger, the right index finger, and the right middle finger. Of these 640 fingers, 263 were
`selected and assigned uniquely to the users in the face database. A Linear Discriminant
`Analysis (LDA) based scheme is used for face matching. Eight face images of each
`user were used during the training phase and the remaining two images were used as
`test images. The face matching score vector (of length 263) was computed for each
`test image as follows. The similarity of the test image to the 2104 (263 × 8) training
`images in the database was found and the largest of the 8 scores of a particular user
`
`7
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`

`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`was selected as the matching score for that user. Fingerprint matching was done using
`minutia features [15]. Two fingerprint impressions of each user were used as templates
`and the other two impressions were used for testing. The fingerprint matching score
`for a particular user was computed as the average of the scores obtained by matching
`the test impression against the two templates of that user. Thus, a fingerprint matching
`score vector for each test impression was computed. The separation of the face and
`fingerprint databases into training and test sets, was repeated 20 times and the results
`reported are the average for the 20 trials.
`The ethnicity classifier proposed in [8] was used in our experiments. This classifier
`identifies the ethnicity of a test user as either Asian or non-Asian with an accuracy
`of 96.3%. If a “reject” option is introduced, the probability of making an incorrect
`classification is reduced to less than 1%, at the expense of rejecting 20% of the test
`images. A gender classifier was built following the same methodology used in [8] for
`ethnicity classification. The accuracy of the gender classifier without the “reject” option
`was 89.6% and the introduction of the “reject” option reduces the probability of making
`an incorrect classification to less than 2%. In cases where the ethnicity or the gender
`classifier cannot make a reliable decision, the corresponding information is not utilized
`for updating the matching score of the primary biometric system.
`Since we did not have the height information about the users in the database, we
`randomly assigned a height ‘Hi’ to user ωi, where the Hi’s are drawn from a Gaussian
`distribution with mean 165 cm and a standard deviation of 15 cm. The height of a user
`measured during the recognition phase will not be equal to the true height of that user
`stored in the database due to the errors in measurement and the variation in the user’s
`height over time. Therefore, it is reasonable to assume that the measured height H∗
`i will
`follow a Gaussian distribution with a mean Hi cm and a standard deviation of 5 cm.
`Let P (ωi|s) be the posterior probability that the test user is user ωi given the pri-
`mary biometric score ‘s’ of the test user. Let yi = (Gi, Ei, Hi) be the soft biometric
`feature vector corresponding to the user ωi, where Gi, Ei, and Hi are the true values
`of gender, ethnicity, and height of ωi. Let y∗ = (G∗, E∗, H∗) be the observed soft
`biometric feature vector of the test user, where G∗ is the observed gender, E∗ is the ob-
`served ethnicity, and H∗ is the observed height. Now the final score after considering
`the observed soft biometric characteristics is computed as:
`gi(s, y∗) = a0 log P (ωi|s)+ a1 log p(H∗|Hi)+ a2 log P (G∗|Gi)+ a3 log P (E∗|Ei) ,
`where a2 = 0 if G∗ =“reject”, and a3 = 0 if E∗ =“reject”.
`
`Experiments were conducted on three primary biometric systems, namely, finger-
`print, face, and a multimodal system using face and fingerprint as the individual modal-
`ities. Figure 4 shows the Cumulative Match Characteristic (CMC) of the fingerprint
`biometric system operating in the identification mode, and the improvement in perfor-
`mance achieved after the utilization of soft biometric information. The weights assigned
`to the primary and soft biometric traits were selected experimentally such that the per-
`formance gain is maximized. However, no formal procedure was used and an exhaustive
`search of all possible sets of weights was not attempted. The use of ethnicity and gender
`information along with the fingerprint leads to an improvement of 1% in the rank one
`
`8
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`

`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`performance as shown in Figures 4(a)and 4(b), respectively. From Figure 4(c), we can
`observe that the height information of the user is more discriminative than gender and
`ethnicity, and leads to a 2.5% improvement in the rank one performance. The combined
`use of all the three soft biometric traits results in an improvement of approximately 5%
`over the primary biometric system as shown in Figure 4(d).
`
`(a)
`
`(c)
`
`(b)
`
`(d)
`
`Fig. 4. Improvement in identification performance of fingerprint system after utilization of soft
`biometric traits.
`
`The ethnicity and gender information did not provide any improvement in the per-
`formance of a face recognition system. This may be due to the fact that the gender
`and ethnicity classifiers, and the face recognition system use the same representation,
`namely, LDA for classification. The LDA algorithm for all the three classifiers operates
`on the same set of training images and hence it is highly likely that the features used for
`these classification problems are strongly correlated. However, the height information
`
`9
`
`Fingerprint
`Fingerprint + Ethnicity
`
`a0 = 0.9, a3 = 0.1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`n
`
`94
`
`93
`
`92
`
`91
`
`90
`
`89
`
`88
`
`87
`
`86
`1
`
`P(The true user is in the top n matches) %
`
`
`
`Fingerprint
`Fingerprint + Gender
`
`a0 = 0.9, a2 = 0.1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`n
`
`93
`
`92
`
`91
`
`90
`
`89
`
`88
`
`87
`
`86
`1
`
`P(The true user is in the top n matches) %
`
`Fingerprint
`Fingerprint + Height
`
`a0 = 0.9, a1 = 0.1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`n
`
`95
`
`94
`
`93
`
`92
`
`91
`
`90
`
`89
`
`88
`
`87
`
`86
`1
`
`P(The true user is in the top n matches) %
`
`Fingerprint
`Fingerprint + Ethnicity + Gender + Height
`
`a0 = 0.76, a1 = 0.08, a2 = 0.08, a3 = 0.08
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`n
`
`96
`
`95
`
`94
`
`93
`
`92
`
`91
`
`90
`
`89
`
`88
`
`87
`
`86
`1
`
`P(The true user is in the top n matches) %
`
`

`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`Fig. 5. Improvement in identification performance of (face + fingerprint) multimodal system after
`utilization of the height of the user.
`
`is independent of the facial features and, hence, it leads to an improvement of 5% in
`the face recognition performance (see Figure 5). The failure of the ethnicity and gender
`information to improve the face recognition performance establishes that fact that soft
`biometric traits would help in recognition only if the identity information provided by
`them is complementary to that of the primary biometric identifier.
`Figure 5 shows the CMC curves for a multimodal system using face and fingerprint
`as the individual modalities. In this system, the combined matching score of the primary
`biometric system is computed as a weighted average of the scores of the face and fin-
`gerprint modalities. We can observe that the rank one performance of this multimodal
`system is superior to that of the individual modalities by 8%. The addition of height
`as a soft biometric feature further improves the performance by 2%. This shows soft
`biometric traits can be useful even if the primary biometric system already has a high
`accuracy.
`
`5 Summary and Future Directions
`
`We have demonstrated that the utilization of ancillary user information like gender,
`height, and ethnicity can improve the performance of the traditional biometric systems.
`Although the soft biometric characteristics are not as permanent and reliable as the tra-
`ditional biometric identifiers like fingerprint, they provide some information about the
`identity of the user that leads to higher accuracy in establishing the user identity. We
`have also shown that soft biometric characteristics would help only if they are com-
`plementary to the primary biometric traits. However, an optimum weighting scheme
`based the discriminative abilities of the primary and the soft biometric traits is needed
`to achieve an improvement in recognition performance.
`Our future research work will involve establishing a more formal procedure to de-
`termine the optimal set of weights for the soft biometric characteristics based on their
`
`10
`
`Face
`Fingerprint
`(0.95 * Face + 0.05 * Fingerprint) + Height
`0.95 * Face + 0.05 * Fingerprint
`Face + Height
`
`2
`
`3
`
`4
`
`5
`
`6
`
`7
`
`8
`
`9
`
`10
`
`n
`
`100
`
`95
`
`90
`
`85
`
`80
`
`75
`
`70
`
`65
`
`60
`
`55
`1
`
`P(The true user is in the top n matches) %
`
`

`

`Proceedings of Biometric Authentication Workshop, LNCS 3087, pp. 259-269,
`Prague, May 2004
`
`distinctiveness and permanence. Methods to incorporate time-varying soft biometric in-
`formation such as age and weight into the soft biometric framework will be studied. The
`effectiveness of utilizing the soft biometric information for “indexing” and “filtering”
`of large biometric databases must be studied. Finally, more accurate mechanisms must
`be developed for automatic extraction of soft biometric traits.
`
`References
`
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