Soft Biometrics: an Asset for Personal Recognition

Soft Biometric: An Asset for Personal Recognition

Chander Kant [1], Rajender Nath [2] [1] Lecturer, [2] Reader Department of Computer Science and Applications K.U., Kurukshetra, Haryana, INDIA [ckverma, rnath_2k3]@ rediffmail.com

Abstract Biometric systems automatically recognize individuals systems recognize users based on their physiological and based on their physiological and behavioral characteristics. behavioral characteristics [1]. Unimodal biometric systems Hence, the fundamental requirement of any biometric make use of a single biometric trait for user recognition. recognition system is a human trait having several desirable . properties like universality, distinctiveness, permanence, acceptability. However, a human characteristic that Methods Examples Problems possesses all these properties has not yet been identified. As What you Know Password, PIN, Forgotten, Shared, a result, none of the existing biometric systems provide ID easy to guess perfect recognition and there is a scope for improving the What you have Key, Cards, etc Lost or Stolen, performance of these systems. Although characteristics like Can be duplicated gender, age, height, eye color and weight are not unique and What you are Fingerprint, Face, Non-repudiable reliable, they provide some information about the user. We Iris… authentication refer to these characteristics as “soft” biometric traits and Table 1: Identification Technologies argue that these traits can be integrated with the identity information provided by the primary biometric identifiers It is difficult to achieve very high recognition rates using like fingerprint, face, iris, signature etc. We propose the unimodal systems due to problems like noisy sensor data utilization of “soft” biometric traits like gender, height, and non-universality or lack of distinctiveness of the chosen weight, age, and ethnicity to complement the identity biometric trait. Multimodal biometric systems address some information provided by the primary biometric identifiers. of these problems by combining evidence obtained from Although soft biometric characteristics lack the multiple sources [2]. A multimodal biometric system that distinctiveness and permanence to identify an individual utilizes a number of different biometric identifiers like face, uniquely and reliably yet they provide some evidence about fingerprint, hand-geometry, and iris can be more robust to the user identity that could be beneficial. This paper noise and minimize the problem of non-universality and presents a framework for integrating the ancillary lack of distinctiveness. However, a multimodal system will information (soft biometrics) with the primary biometric require a longer verification time thereby causing system. Algorithm based of existing data and facts show inconvenience to the users. A multimodal biometric system that the recognition performance of an identification system based on different biometric identifiers can be expected to can be improved significantly by using additional user be more robust to noise, address the problem of non- information like gender, ethnicity, and height. universality, improve the matching accuracy, and provide reasonable protection against spoof attacks. However, such Keywords: primary biometric, soft biometric, universality, a system will have two limitations. Firstly, the overall cost distinctiveness, permanence, acceptability. involved in building the multimodal system can be high due to the need for multiple high quality sensors and increased storage and computational requirements. Secondly, the 1. Introduction system will require a longer verification time thereby causing inconvenience to the users. Due to these limitations, There are a number of methods to verify identity adopted by the number of identifiers in a multimodal biometric system society or automated systems. These are summarized in is usually restricted to two or three. Table1. We define soft biometric traits as characteristics that A possible solution to the problem of designing a reliable provide some information about the individual, but lack the and user-friendly biometric system is to use additional distinctiveness and permanence to sufficiently differentiate information about the user like height, weight, age, gender, any two individuals. The soft biometric traits can either be ethnicity, and eye color to improve the performance of the continuous (e.g., height and weight) or discrete (e.g., primary biometric system. Most practical biometric systems gender, eye color, ethnicity, etc.). In this paper, we describe collect such additional information about the users during a framework for integrating the information provided by the enrollment. This information is stored either in the database soft biometric with the primary biometric system. Biometric

1 or in the smart cards possessed by the user. However, this interacting user. For example, if the user can somehow be information is not currently utilized during the automatic identified as a middle-aged male, the search can be identification/verification phase. This motivates us to utilize restricted only to the subjects with this profile enrolled in every available information about the user to improve the the database. This greatly improves the speed or the search performance of a biometric recognition system. Further, efficiency of the biometric system. Filtering and system biometric systems used in access control applications parameters tuning require an accurate classification of a user generally have a human supervisor who oversees the into a particular class (e.g., male or female, blue or brown operations of the system. When a genuine user is falsely eyes, Caucasian or Asian or African). This requires a pre- rejected by the system, the human operator steps in to verify identification module that can accurately perform this the identity of this user. This manual verification is usually classification. The probability of user ώi, given by primary done by comparing the facial appearance of the user with biometric feature vector x and the soft biometric feature the facial image appearing on the user’s identification card vector y i.e., P(ώi | x; y) can be calculated using the Bayes’ and by verifying other information on the ID card like age, rule. gender, height, and other visible identification marks. If the soft biometric characteristics can be automatically extracted P(ώi|x; y) = p(y|ώi) P(ώi|x) and used during the decision making process, the overall ------performance of the system will improve and the need of for i=1 to n p(y|ώi) P(ώi|x) manual involvement will be reduced. where P(ώi | x) is the probability that the test user is ώi 2. Related Work given the feature vector x. If the output of the primary Integrating Soft Biometrics with Primary Biometrics to biometric system is a matching score, it is converted into posteriori probability using an appropriate transformation. improve FAR and FRR. For the secondary biometric system, we can consider P(ώi | The first personal identification system developed by x) as the prior probability of the test user being user ώi. Alphonse Bertillon [3] for identification of criminals was based on three sets of features: (i) Anthropometric The logarithm of P(ώi|x; y) in equation (3) can be measurements like height and length of the arm, (ii) expressed as Morphological description of the appearance and body log P(ώi|x, y) = log p(y1|ώi) + ……. + log p(yk|ώi) shape like eye color and anomalies of the fingers, and (iii) + log P(yk+1|ώi) + …… +log P(ym|ώi) + log P(ώi| Peculiar marks observed on the body like moles and scars. x) – log p(y) Although the Bertillon system was useful in tracking This formulation has two main drawbacks. The first criminals, it had an unacceptably high error rate because the problem is that all the m soft biometric variables have been features used are indistinctive and non-permanent. weighed equally. For example, the gender of a person may Heckathorn et al. [4] have shown that a combination of give more information about a person than height. personal attributes like age, gender, eye color, height, and Therefore, we must introduce a weighting scheme for the other visible identification marks can be used to identify an soft biometric traits. individual only with a limited accuracy. Hence, a system that is completely based on soft biometric traits cannot meet the accuracy requirements of real world applications. However, soft biometric traits can be used to improve the performance of traditional biometric systems.

Wayman [5] proposed the use of soft biometric traits like gender and age, for filtering a large biometric database (Figure1). Filtering refers to limiting the number of entries Figure 2. Improvement in authentication performance after in a database to be searched, based on characteristics of the utilization of soft biometric traits.

2 Another problem is that any impostor can easily spoof the assume that time taken by Primary Biometric System = TF system because the soft characteristics have an equal say in and time taken by Soft Biometric = TS (where TF>TS around the decision as the primary biometric trait. It is relatively 10 times, because soft traits are easily calculated at fast rate easy to modify/hide one’s soft biometric attributes by [6]. In conventional Fingerprint verification system, Total applying cosmetics and wearing other accessories (like Processing Time TR = 100000*TF. mask, shoes with high heels, etc.). We can overcome to the In Proposed system Tp = (100000-100)* TS + 100 *(TF + TS). above mentioned problem with some way [9] and can get (Since 99900 templates are discarded at Level-I. Only 100 the better result as shown in figure [2]. templates are need verified at both of levels).If ratio of TF: TS is 10: 1 then 3. Proposed work TR = 100000*10=1000000 and Integrating Soft Biometrics with Primary Biometrics to Tp = 99900 + 100(10+1) = 99900+1100=101000. Ratio R= T T = 1000000/101000= 9.9 times that is apx 10 reduce process time R / p times. In our framework, the biometric recognition system is i.e. total process time of proposed system increases 10 times divided into two subsystems. One subsystem is called the compared to conventional system. Further this ratio primary biometric system and it is based on traditional becomes more when more templates are taken at input side. biometric identifiers like fingerprint, face and hand- This paper presents an approach that improves the geometry [7]. The second subsystem, referred to as the processing time of existing conventional fingerprint secondary biometric system, is based on soft biometric traits verification system. The approach is useful when fingerprint like age, gender, and height. Figure [3] shows the verifications are made at large scale level (off-line). The architecture of a personal identification system that makes proposed scheme is not free from all drawbacks, as it needs use of both fingerprint and soft biometric measurements. extra storage space to store the templates with soft trait data The combination of such system is also discussed earlier as like age, gender, height. discussed in previous section of this paper but here we have put soft biometrics as first stage and output of first stage is 4. Extraction of Soft Biometric fed into second stage that is based on primary biometric In order to utilize soft biometrics, there must be a system. This type of combination will reduce the processing mechanism to automatically extract these features from the time system and will improve the efficiency of system. user during the recognition phase. As the user interacts with Processing at Stage-II will be performed only when the user the primary biometric system, the system should be able to will pass the stage-I else there will be rejection automatically measure the soft biometric characteristics like automatically even without computing the stage-II. Stage-I height, age and gender without any interaction with the user. automatically computes the soft traits like age, gender, This can be achieved using a special system of sensors. For height of user. example, a bundle of infra-red beams could be used to measure the height. A camera could be used for obtaining the facial image of the user, from which information like age, gender, and ethnicity could be derived [8]. These observed soft biometrics information could then be used to supplement the identity information provided by the user’s primary biometric identifier. Extensive studies have been made to identify the gender, ethnicity, and pose of the users from their facial images. Gutta et al.[10] proposed a mixture (Figure3. Architecture of Personal Identification using of experts consisting of ensembles of radial basis functions Primary Biometrics and Soft Biometric) for the classification of gender, ethnic origin, and pose of human faces. Their gender classifier classified users as Experimental results show significant improvement in either male or female with an average accuracy rate of 96%. recognition performance due to the utilization of soft Age determination is a more difficult problem due to the biometric information. We used fingerprint as the primary very limited physiological or behavioral changes in the biometric identifier and age, gender, and height as the soft human body as the person grows from one age group to biometric variables. We have chosen soft traits in such a another. There are currently no reliable biometric indicators way that they could be easily computed automatically. for age determination. Let us illustrate the improvement in Total Response Time with the help of an example. Suppose, there are 100000 templates to be compared and matched and out of these 100000 templates there are only 100 templates which are supposed to be matched against the input templates. Let us 5. Summary and Future Prospectus

3 [4] Heckathorn, D.D., Broadhead, R.S., Sergeyev, B.: A We have demonstrated that the utilization of ancillary user Methodology for Reducing Respondent Duplication and information like gender, height, and ethnicity can improve the performance of the traditional biometric systems. Impersonation in Samples of Hidden Populations. In: Annual Although the soft biometric characteristics are not as Meeting of the American Sociological Association, Toronto, permanent and reliable as the traditional biometric Canada (1997) identifiers like fingerprint, they provide some information about the identity of the user that leads to higher accuracy in [5] Wayman, J.L.: Large-scale Civilian Biometric Systems - Issues establishing the user identity. We have also shown that soft and Feasibility. In: Proceedings of Card Tech / Secur Tech ID. biometric characteristics would help only if they are (1997) complementary to the primary biometric traits. However, an optimum weighting scheme based the discriminative [6] Jain, A.K., Dass, S.C., Nandakumar, K.: Can soft biometric abilities of the primary and the soft biometric traits is traits assist user recognition? In: Proceedings of SPIE International needed to achieve an improvement in recognition Symposium on Defense and Security: Biometric Technology for performance. Our future work in this direction will involve the improvement of proposed system free from the problems Human Identification (To appear). (2004) discusses in section 3. Also more accurate mechanism must [7] Jain, A.K., Dass, S.C., Nandakumar, K.: Integrating Faces, be developed for automatic extraction of soft biometric Fingerprints, and Soft Biometric Traits for User Recognition. traits. Proceedings of Biometric Authentication Workshop, LNCS 3087, 6. References pp. 259-269, Prague, May 2004 [1] Jain, A.K., Bolle, R., Pankanti, S., eds.: Biometrics: Personal [8] A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, “An identity Identification in Networked Security. Kluwer Academic Publishers authentication system using fingerprints,” Proceedings of the IEEE (1999). 85(9), pp. 1365–1388, 1997. [2] Hong, L., Jain, A.K., Pankanti, S.: Can Multibiometrics [9] Jain, A.K., Dass, S.C., Nandakumar, K.: Integrating Faces, Improve Performance? In: Proceedings of IEEE Workshop on Fingerprints, and Soft Biometric Traits for User Recognition. Automatic Identification Advanced Technologies, New Jersey, Proceedings of Biometric Authentication Workshop, LNCS 3087, U.S.A. (1999) 59–64 pp. 259-269, Prague, May 2004. [3] Bertillon, A.: Signaletic Instructions including the theory and [10] S. Gutta, J. R. J. Huang, P. Jonathon, and H. Wechsler, practice of Anthropometrical Identification, R.W. McClaughry “Mixture of Experts for Classification of Gender, Ethnic Origin, Translation. The Werner Company (1896) and Pose of Human Faces,” IEEE Transactions on Neural Networks 11, pp. 948–960, July 2000.