The Challenges of the Environment and the Human / Biometric Device Interaction on Biometric System Performance

Stephen J. Elliott, Ph.D., Eric P. Kukula Nathan C. Sickler

Biometric Standards, Performance, and Assurance Laboratory Department of Industrial Technology,

Purdue University West Lafayette, IN 47906, US

sjelliott@tech.purdue.edu epkukula@tech.purdue.edu sicklern@purdue.edu

Abstract

This paper outlines various research projects that have been conducted at Purdue University in the areas of environment, population, and devices. These areas are of interest as biometric technologies are currently being implemented in various business applications. The environmental research is concerned with the performance of a facial recognition algorithm at differing illumination levels. The second study looks at population, which examines differences in image quality with regard to population age. The third study outlines dynamic signature verification and the issues associated with signing on different digitizers.

1. Introduction Biometric identification technology is defined

as the “automatic identification or identity verification of (living) individuals based on behavioral and physiological characteristics [1]. Biometric research centers on five fundamental areas: data collection, signal processing, decision-making, transmission, and storage. Each of these areas has specific challenges associated with them. The research described below examines some of the problems associated with biometric authentication, namely the environment, population, and devices. These research areas are fundamental, because the widespread adoption of biometric technologies requires an understanding of how each of these factors can affect the performance of the device. Furthermore, it is also significant for both government and industry to have research disseminated on how biometric samples perform over time, the resulting effects on system

performance, and how the image quality of a biometric sample changes with age.

Furthermore, all of these factors may be compounded by changes in the environment, such as the effects of variances in lighting, or camera placement with respect to face identification [13]. The Face Recognition Vendor Test (FRVT) assessed if recent advancements in facial recognition systems actually improved performance. Ten vendors participated in this test, which concluded that variation in outdoor lighting conditions, even with images that were collected on the same day, drastically reduced system performance. Specifically, the verification performance for the best face recognition systems drops from 95% to 54% going from indoors to outdoors [2]. This poses the fundamental question: if an individual enrolls at a drivers license bureau, then has their photograph taken in an environment with controlled illumination for identification purposes, facial recognition systems will verify that individual 95% of the time in that environment. However, if the verification attempt occurs outdoors, the facial recognition system performance would fail about 46% of the time on the same day, even on the same day as enrollment, even eliminating template aging as a factor. In most real world scenarios, an individual will not be identified in the same environment or on the same day as they enrolled; therefore more research is needed in this area to assess system performance in a variety of conditions.

Second, we have limited knowledge in the gathering of biometric templates in an aging population (62 and older) and how this may affect system performance as well as image quality. Sickler and Elliott [2] found in preliminary tests of fingerprinting, that elderly have poor ridge definition and lower moisture

content, resulting in drier images that are of lower quality than younger individuals (18-25 year olds). Again, this is of significance as some states are implementing fingerprint recognition for social services, as well as for drivers licenses.

Third, there is a need to see how biometric samples change when collected on different devices. In [3], over 15,000 dynamic signatures were collected on different mobile computing devices, with the resulting signatures analyzed to see which variables are stable over different digitizers. The purpose of this research was to test signature verification software on both traditional digitizer tables and on wireless/mobile computing devices, in order to assess how the dynamics of the signature signing on such devices change. Specifically, the researcher examined the differences on table-based digitizers (Wacom Intuit and the e-Pad), and over mobile computing devices (Palm IIIxe, Symbol 1500 and 1740 devices). The conclusion of the research was that was significant differences in specific variables across different digitizers. Therefore, when deciding on implementing dynamic signature verification using different digitizers, the device type and identity needs to be attached within the signature data. Therefore, the study of the environment, population, and device challenges is important to the field of biometrics, and is an area of study currently being undertaken at Purdue University. 2. Biometric Performance and the Environment.

Numerous experts, reports and papers have

stated that illumination greatly affects facial recognition performance, but have not provided significant results that have showed this [4-12]. Kukula and Elliott [13] evaluated the performance of a commercially available 2D face recognition algorithm across three illumination levels. The illumination levels were determined by collecting data for sixty minutes in 2 locations: a local campus restaurant (low

light) and from the Department of Industrial Technology office (high light). The third light level (medium light), was determined by taking the mean of the other two light levels. Figure 1 shows sample images illustrating the differences between the three illumination levels.

Kukula and Elliott completed a study that consisted of thirty individuals, of which 73% were male, between the ages of 20 and 29 and Caucasian. Other represented ethnicities were Hispanic and Asian/Pacific Islander. 30% of the population had facial hair, 24% wore glasses, and 6% wore hats. Two participants also had problems during enrollment, which was attributable to the subjects wearing a hat; but when it was removed, enrollment was successful. There were 6 enrollment failures out of 96 attempts. Therefore, the overall failure to enroll (FTE) rate was 6.25%. The failure to acquire (FTA) rates for low light (7-12 lux), medium light (407-412 lux), and high light (800-815 lux) were 0.92%, 0.65%, and 0%, respectively [13]. The statistical analysis revealed that at a high illumination enrollment, the illumination of the verification attempt was not statistically significant, based on the three tested illumination levels; low (7-12 lux), medium (407 – 412 lux), and high (800 – 815) lux. This signifies that when your lighting conditions are not constant for verification, the enrollment light level should be as high as possible. For the low and medium enrollments, the illumination used for the verification attempts was statistically significant, which meant that enrollments using low and medium illumination, defined for this evaluation, are not good to use when your environmental lighting conditions are not constant for verification. This research also revealed that there was a statistically significant difference between enrollment illumination level and the verification illumination level at � = 0.01, insinuating that the enrollment illumination level is a better indicator of performance than the illumination level of the verification attempts.

Low Light - 8 lux

High Light - 800 lux

Medium Light – 423 lux

Figure 1: Sample Verification Images

3. Fingerprint Image Quality and the Elderly

Discussion of poor image quality issues regarding elderly fingerprints occurs in the biometric literature [14-18]. Recently, an Ad Hoc group was formed by INCITS M1 to address image quality issues, and a working document has been drafted which addresses two types of image quality: effectiveness quality and fidelity quality. Effectiveness image quality is a score that determines the usability of an image from the biometric system standpoint, whereas fidelity image quality determines how closely images of the same individual match, without regard for system usability. In the research addressed in this paper, effectiveness image quality is a more robust measure, since poor image quality issues pose problems for fingerprint recognition systems during the enrollment, verification, and identification processes. Therefore, the problem proposed by [2] was to determine the impact that particular variables, namely age and moisture, had on the effectiveness quality of fingerprint images.

Non-uniform and irreproducible contact between the fingerprint and the platen of a fingerprint sensor can result in an image with poor effectiveness quality (Figures 2, 3 & 4). Non-uniform contact can result when the presented fingerprint is too dry (Figure 3) or too wet, and irreproducible contact occurs when the fingerprint ridges are semi-permanently or permanently changed due to manual labor, injuries, disease, scars or other circumstances such as loose skin [19].

Figure 2. (a). Dry image (left) of low quality and a (b).normal image (right) of high quality. Both images were acquired from an optical sensor [2]

Figure 3. Visual display of the effectiveness quality of the low quality dry image. A green area has good image quality, and all other colors indicate poor image quality.

Figure 4. Visual display of the effectiveness quality of the high quality normal image. A green area has good image quality, and all other colors indicate poor image quality.

These two contact issues can result when an

elderly user (62 and older) presents their fingerprint to the fingerprint device [19]. As individuals age, their skin becomes dryer, sags from the loss of collagen, and becomes thinner and loses fat due to the loss of elastin fibers, which decreases the firmness of the skin [20], and is likely to have incurred semi-permanent or permanent damage over the life of the individual. Two areas of the fingerprint recognition system, data collection and signal processing, are affected by non-uniform contact, irreproducible contact, and the inability of interaction between the system and the user.

The problem of interaction between the user and the system affects the sub-category of

presentation within the data collection silo. If the user cannot present their fingerprint to the device due to arthritis or other age related factors, then enrollment and subsequent verification or identification attempts are not possible through fingerprint recognition. Irreproducible and non-uniform contacts affect the sub-categories of feature extraction and quality control, within the signal-processing silo. Non-uniform contact tends to produce images of low quality, resulting in poor feature extraction of the presented fingerprint. Factors responsible for irreproducible contact, such as a temporary injury, introduce false minutiae points, creating an inaccurate representation of the individual’s fingerprint, therefore reducing the effectiveness quality of the fingerprint image. Furthermore, the effectiveness quality of a captured image is one of the most important aspects for a biometric system, as it is this quality parameter that determines whether a captured image is acceptable for further use within the biometric system, or not. The effectiveness quality of a presented fingerprint image is developed and processed by the quality control function of the biometric system, and a score, based on the image’s usability, is assigned to that image. It is these quality scores and captured images that provide the data used by the biometric system to determine an accept/reject decision.

For this study two population age groups were used, the elderly (62+) and a younger (18-25 years) group, although no subject was excluded from participating based on age. However, data from subjects not falling into one of these age groups were excluded from the analysis. The minimum age was set to 18 years old since individuals this age and older are considered adults and do not need a guardian’s consent to participate. The maximum age of the younger population was set to 25 years old, in order to establish the typical age range for college or university students. Two different devices were used in the research, capacitance and optical fingerprint sensors. The population size was 54 in both the 18-25 and 62+ categories.

The results show that the difference of effectiveness image quality data was statistically significant at α=0.01 for each index finger, as well as for each sensor. Therefore, the hypothesis stating that there is no statistically significant difference in the fingerprint quality between the age groups 18-25 and 62+ is rejected at α=0.01. The basic findings are shown graphically in Figure 5, the Pearson correlation of image quality vs. age (ratio).

Figure 5. Example Pearson correlation of image quality vs. age (ratio).

The second hypothesis stated that there is no

statistically significant difference between the fingerprint moisture content of the age groups 18-25 and 62+. This hypothesis was rejected at α=0.01 for both index fingers when used with the optical sensor and it was rejected for the right index finger in conjunction with the capacitance sensor. However, this hypothesis failed to be rejected at α=0.01 for the left index finger and the capacitance device. The overall finding of this study implies that more emphasis should be placed on an individual’s age, rather than the moisture of the finger when developing a fingerprint recognition system.

4. Device Impacts on Dynamic Signature Verification.

The use of written signature as a symbol for business and personal transactions has a long history. Before the collection of data, a decision on the acquisition of the signature signal is critical to the capture of the signature: on-line or dynamic signature verification has various methods of acquiring data. Each of the various devices used in studies demonstrate different physical and measurement characteristics. According to [21]there is no consensus within the research community on the best method of capturing this information, although the table-based digitizer tablet is by far the most popular. As the hardware progresses towards providing solutions to mobile and electronic commerce, there may be a shift away from these table-based digitizers.

Hardware properties can affect the variables collected in the data acquisition process, and

therefore the quality and performance of the device. Different digitizers and software enable the capture of different parameters of the signature, at differing resolutions and speed. Typical acquisition features include 'equivalent-time-interval-sampled, x, y co-ordinates of pen-tip movements, pen pressure, pen altitude (the height of the pen from the digitizer) and pen azimuth (the angle between the reference line and the line connecting a point with the origin) [22].

The central focus of [3] was to examine whether there are statistically significant differences in the measurable variables across devices. The volunteer crew was made up of 203 individuals whose demographics tended toward the 19-26 age groups due to the composition of the testing environment. Although not representative of the U.S. population except for gender, it is representative of the population found in college and university environments. Males accounted for 66% of the volunteer group, and females 35%, who were typically older than the males. Right handed members of the population accounted for 91%, and left-handed members accounted for 9%.

Based upon the data, the major conclusion that can be drawn from the study is that there are significant differences in the variables across devices, yet these variables are not significantly different within device families (Wacom, Palm, and Interlink E-pad). When these devices are grouped together, these variable differences continue to be significant. Dynamic signature verification is used within the realms of electronic document management and contracts, and will typically be verified only if the document validity is questioned. As a result, the type of device needs to be attached in some way to the signature data so that the document

examiners can compare signatures captured on the same type of device.

5. Conclusion

Environment, image quality, and device selection play an important role in the successful implementation of a biometric system. The research shown here indicates that there are still some challenges associated with biometrics, but that these challenges can be overcome through algorithm improvements. The results of the face recognition evaluation showed there are still significant challenges with regard to illumination levels and face recognition especially at lower light levels, which correlates with other research that has been done [4-12]. Further research is planned that will examine the performance of a three dimensional face recognition algorithm across three illumination levels, to examine how 3D face algorithms respond to illumination changes. For image quality and fingerprint recognition, further research is planned that includes other sensors such as optical and thermal technologies, as well as swipe-based capacitance sensors, small area capacitance sensors, and different forms of silicon sensing chips, such as radio frequency based sensors. Also, a future study will examine the image quality of fingerprints collected on devices in different outdoor settings. The results of such a study would be important in understanding if image quality is increased or decreased over devices depending on the time of year or season. For signature verification, a further study into forgeries will be undertaken shortly, that establishes which variables in a dynamic signature verification algorithm are susceptible to forgery.

6. References

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