self adaptive biometric systems

Self Adaptive Systems: An Experimental Analysis of the Performance Over Time

Ajita Rattani, Gian Luca Marcialis, Fabio Roli

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Biometric Verification Systems

The performance of biometric systems degrades quickly when input images exhibit substantial variations compared to the enrolled “templates”

Some face examples showing intra-class variations in input data un-represented by enrolled template

TEMPLATE

Image is acquired for each user (gallery) in controlled environment (for instance ISO/ IEC FCD 19794-5 standard)

“Template” is created and identity labels assigned

They operate in two distinct stages:

1) The enrolment stage

2) The verification stage.

QUERY IMAGES

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Initial Attempts to Increase Template Representativeness

• Re-enrollment

• Multibiometrics (Handbook of Multi-biometrics, 2006)

• Virtual biometric template synthesis (pose correction, illumination correction, de-ageing transformations) (Wang et al. 2006, Geng et al. 2007)

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Recent Introduction- Template update Methods

• Characteristics:– Adapt themselves to the intra-class variation of the input

data.

– Minimizing performance loss due to unrepresentative and outdated templates

• Commonly adopted is self-adaptive systems.

Self-Adaptive Systems

• Highly confidently classified samples are used for adaptation

• In order to avoid impostor introduction

• Claimed to be robust against short and medium term intra-class variations

No

No

No

No

Impostor

5 months

41x100

100x8

100x8

12x200

Database

FacePavani et al.

FingerRyu et al.

FaceRoli et al

FingerX. Jiang

and W. Ser

ModalityReference

Till date: No paper has shown the performance robustness over time

Reason: Unavailability of large number of samples collected over a period of time, per user basis

Assumption of absence of impostor

No theoretical explanation of the functioning

State-of-the-art

Contributions

– This is the first study evaluating the performance of self-adaptive systems, on the input batch of samples as available over time

– The conceptual explanation of the functioning of self-adaptive systems, supported by experimental validations

– DIEE multimodal database has been explicitly collected for this aim, over a span of 1.5 years

Conceptual representation

A hypothetical diagram showing the initial condition where the enrolled template is shown with the help of star and encircled in its representation region

The representational capability of each template in the updated set on adaptation using samples 1, 2 and 3

Contd...

As a result overall genuine region expands

In the real time environment impostor samples may also be present

Experimental Validations

• Dataset: DIEE Multimodal database– 49 subjects with 50 samples per subject acquired in five

sessions with 10 samples per session

– Acquired in a time span of 1.5 years

– Containing temporal as well as other intra-class variations

Example facial images taken from two different sessions for a randomnly choosen user

Experimental Protocol

• Training: 2 enrolled images per person from the batch b_1

• Updating: batches two to four

• Performance evaluation:On updating using batch b_i performance is evaluated for batch b_i+1and EER_i computed

Experiment #1

• Aim: to evaluate the performance of self adaptive systems over time

• Assumption of absence of impostor’s access.

• Updating threshold: 0.01 % FAR

Results

Performance of self-adaptive systems for index and thumbprint biometrics in comparison to baseline classifier

Performance enhancement and stability can be attained over time

At varying threshold conditions

Performance of self-adaptive face recognition system at varying thresholds from stringent to relaxed for face biometrics

Large variation in the performance

from one updating cycle to

another as a result of

representation region expansion

significantly

Table: showing percentage of samples gradually

added to the user’s gallery for face biometrics

75.3073.6668.5160.81 %

70.9868.3162.0356.790.1 %

65.7461.8355.8652.160.01 %

27.7026.9526.5433.30.00001 %

19.3619.7521.6031.170.00001 %

(%) Cycle 4(%) Cycle 3(%) Cycle 2(%) Cycle 1Threshold at % FAR

Further confirmation:- representation region expansion

The scores obtained on fifth batch using the baseline matcher enrolled with two templates from a random user for thumb biometric

The scores obtained on fifth batch using the self-adaptive system updated using 1 to 4 batches for a random user for thumb biometric

Experiment #2

• Aim: is to evaluate the performance of self adaptive systems over time on the assumption of presence of impostor’s access.

• Assumption: Presence of impostors, enabling the evaluation of impostor’s intrusion over time

• Updating Threshold: 0.001 % FAR

Performance of self adaptive thumb and index fingerprint system under the assumption of impostor presence

•Performance strongly suffers from the operation at stringent threshold

•However, the stability can be obtained over time

•Different biometrics show difference trend in performance improvement

Conclusions and Future work

• Self-adaptive systems can result in performance enhancement and classifier’s stability over time

• The obtained performance enhancement is very much dependent on the set updating threshold

• Different biometric may show different performance trend over time.

• The possibility of presence and updating due to impostor is a serious and open issue.

• Future work will rely on further development of the conceptual behaviour and significant in-depth analysis of impostor’s effect over time.