Multimodal Biometric Data Acquisition Platform

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the data capture phase where the multimodal biometric database is created, and the .... description of the palm contour which is free of noise (see Fig. 4) ...
POLYBIO: Multimodal Biometric Data Acquisition Platform and Security System Anastasis Kounoudes , Nicolas Tsapatsoulis2, Zenonas Theodosiou1, and Marios Milis1 1

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SignalGeneriX Ltd, Arch.Leontiou A’ Maximos Court B’, 3rd floor, P.O.Box 51341, 3504, Limassol, Cyprus {tasos, z.theodosiou, milis}@signalgenerix.com 2 Cyprus University of Technology, Arch.Kyprianos Kyprianos, P.O.Box 50329, 3603, Limmasol, Cyprus [email protected]

Abstract. Biometrics is the automated method of recognizing a person based on a physiological or behavioural characteristic. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. In the last few years there is increasing evidence that technologies based on multimodal biometrics can provide better identification results if proper fusion schemes are accommodated. In this work, we present a novel platform for multimodal biometric acquisition which combines voice, video, fingerprint and palm photo acquisition through an integrated device, and the preliminary fusion experiments on combining the acquired biometrics modalities. The results are encouraging and show clear improvement both in terms of False Acceptance Rate and False Rejection Rates compared to the corresponding single modality approaches. In the current report, fusion was accommodated at the output of the single modalities; however, fusion experimentation is ongoing and further fusion methodologies are under investigation. Keywords: Biometric fusion, Data Acquisition, GUI, Matlab

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Introduction

The emergence of automatic identification of an individual by using certain physiological or behavioral traits, has addressed the 1

problems that plague traditional verification methods such as passwords and ID cards [1]. Biometric authentication requires comparing a registered or enrolled biometric sample. During enrolment a sample of the biometric trait is captured, processed by a computer, and stored for later comparison. A biometric system based on a single biometric identifier for a personal identification is often not able to meet the desired performance requirements. The performance is largely affected by noise in sensed data, nonuniversality, upper bound on identification accuracy, and spoof attacks [2]. Some of the limitations of a biometric system can be addressed by using a consolidation of multiple sources of biometric information [3,4,5]. A multimodal biometric system combines a variety of biometric identifies in making a personal identification and takes the advantage of the capabilities of each individual biometric. Based on the nature of biometric modalities, multibiometric systems can be classified into six categories including multi-sensor, multi-algorithm, multi-instance, multisample, multimodal and hybrid [6]. Multibiometric systems provide a variety of advantages against traditional biometric systems and are able to encounter the performance requirements of various applications [7]. The problem of non-universality is addressed, since sufficient population coverage can be ensured by a multiple traits. Furthermore, multibiometric systems can facilitate the indexing of large-scale databases, can address the problem of noisy data and provide antispoofing measures by making it difficult for an impostor to spoof multiple biometric traits of a legitimate enroll individual. In this paper we present a new multimodal biometric data acquisition platform and security system. The proposed system uses fingerprint, face, voice and palm geometry features of an individual for verification purposes. The paper is organized as follows: Section 2 presents the single modality biometrics for voice fingerprint and hand geometry. Section 3 describes the Biometrics Fusion. The system is detailed in section 4 whereas Section 5 presents the evaluation of the results and related discussion. Finally, conclusions and further work are stated in Section 6.

2 Single modality biometrics Multibiomteric systems use multiple biometric modalities. A brief description of biometrics that used for our system is given below.

2.1 Voice Biometrics / Extraction method Voice is the natural means of communication for human beings thus making it the most convenient to use biometric. In addition, voice needs inexpensive equipment for capturing and can be deployed in a variety of telephone-based or internet-based applications where other biometrics are impossible to be deployed. Voice biometric is utilised in this work in the form of text-dependent Speaker Verification using concatenated phoneme Hidden Markov Models (HMMs) [8]. The experimental setup included the evaluation of the Speaker Verification performance using the traditional Mel Frequency Cepstral Coefficients (MFCC) [9, 10] while future experiments will involve the Perceptual Linear Prediction (PLP) coefficients [11]. The procedure is initiated when the user is text-prompted a series of utterances by the system in order to capture the speech samples. This procedure is repeated both in the data capture phase where the multimodal biometric database is created, and the verification phase where the captured speech of a specific user is verified against his HMM models or Voiceprint. A front-end feature extractor is incorporated to calculate the voice features, which are used for both the enrolment and the speaker verification phase. In the enrolment phase, speaker-specific phoneme models are created for each reference speaker. In the speaker verification phase, the phoneme concatenation model corresponding to the prompted single-digit sequence is constructed, and the accumulated likelihood of the input speech frames for the model is compared with a threshold to decide whether to accept or reject the speaker. In the case of successful speaker verification, the features of the speech signal are stored for updating the HMM models of the specific speaker. The approach is based on a simple vocabulary consisting of a single digit numbers spoken continuously in sequences such as “2-3-5-7-9”. The advantage is that by training HMM models for the phonemes needed to construct all the single-digits of the vocabulary, the method can employ random sequences for authentication, and thus its robustness to impostors is increased. 2.2 Fingerprint Biometrics / Extraction Method Fingerprints are probably the more extensively studied biometric. Uniqueness, permanence, easy acquisition and the small size of the acquisition devices (at least the electronic ones) make fingerprints one of the most popular person identification methods. Usage of fingerprints in verification systems is not so common because fingerprint acquisition has been related, for years, with criminal prosecution and, therefore, it raises user annoyance. This prepossession is getting lower, however, mainly due to the extensive usage of fingerprints for user authentication in popular computing systems such as laptops. Characteristic fingerprint features are generally categorized into three levels [12]: patterns, points and shape. Patterns are the global details of the fingerprint such as ridge flow and pattern type. Although they are not unique, patterns are useful for fingerprint

classification into generic categories such as whorl, left loop, right loop, etc. Points refer to the characteristics or minutiae proposed by Galton [13] and include ridge bifurcations and endings. They have sufficient discriminating power to establish the individuality of fingerprints. Finally, shape features include all dimensional attributes of the ridge such as ridge path deviation, width, pores, edge contour, incipient ridges, breaks, creases, scars, and other permanent details. It is claimed that shape features are permanent, immutable, and unique according to the forensic experts, and if properly utilized, can provide discriminatory information for human identification. In the context of the proposed multibiometric system we do not enter into a sophisticated feature extraction process for the fingerprint biometric. Instead we have tried to combine level 1 (patterns) and level 3 (shape) features through a smart combination of fractal scanning of image points and frequency analysis of these points. The proposed fingerprint feature extraction method is simple through powerful: A signature S (1D vector, see also Fig. 1) is created for each 2D fingerprint image by using the well known Hilbert fractal [14] (see Fig. 2) which is one of the most popular space filling curves. Then the power spectrum PD(S) of the signature is computed over a set of frequency bands (see Fig. 3). The vector of power spectrum values in the various frequency bands is used as feature vector for the fingerprint image. Fingerprint Image Signature

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Luminace value

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Fig. 1: Image signature using the luminance at sampled points

Fractal filling curved superimposed on a fingerprint image

Fig. 2: Hillbert filling curve for 2D points sampling P ower spectrum over several frequency bands

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P ower density (log value)

0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 0

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15 20 Frequency band number

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Fig. 3: Feature vector for a fingerprint image 2.3 Hand Geometry Biometrics / Extraction Method Hand geometry biometric systems are becoming very popular for verification purposes. Although hand geometry is not as unique as other biometrics (e.g., fingerprints), it is permanent and has not been related for criminal prosecution; therefore it is an acceptable method for verification for the great public. In person identification

systems hand geometry has been used mostly as a complement to fingerprints. However, in cases of small user population, hand geometry biometrics are commonly used for authentication since they present acceptable FAR and FRR rates. Hand geometry biometrics fall into two main categories: geometric measurements and contour description. The automatic extraction of geometric measurements from a hand geometry image is a rather difficult error pruned task. The method is more appropriate in a semi automatic environment where a human user indicates the prominent points in the hand contour. Contour description methods have in general lower accuracy but they are more robust in automatic authentication processes. In this study we have adopted a contour description approach because it is faster and fits well in our multibiometric environment. Fourier descriptors [15] provide a means to describe contours. The idea is to represent the contour as a function of one variable, expand the function in terms of its Fourier series, and use the coefficients of the series as the features. Let us assume that the palm boundary coordinates (x(n), y(n)), n = 0, 1, …, N, have been extracted in the preprocessing stage. A complex sequence z(n) is simply generated from the boundary coordinates: z ( n ) = x ( n ) + jy ( n ), n =0,1,..., N −1 (2.3.1) Taking the Discrete Fourier Transform of the sequence z(n) we get: a(k ) =

N −1

∑z(n ) exp( n =0

− j 2πkn ), N

0 ≤ k ≤ N −1

(2.3.2)

a =[a ( 0) a (1) ... a(N −1) ]T (2.3.3) a(k ) The values Fd ( k ) = are called Fourier descriptors (please a note that there are several types of Fourier descriptors; all of are based on the previously stated principle). It can be easily shown that the values Fd(k) are independent of translation, rotation and scaling. In the current work we use a limited subset of the Fourier descriptors as the palm geometry biometric:

aˆ = [ Fd (1) Fd ( 2) ... Fd (M )] T

M T

(k )

(

)