Improved finger-knuckle-printauthentication based on orientationenhancement

A. Morales, C.M. Travieso, M.A. Ferrer and J.B. Alonso

Presented is a new approach to person verification using finger-knuckle-prints (FKPs). It applies a Gabor filter to enhance the FKPinformation and a scale invariant feature transform (SIFT) to extractthe features. Experiments with the most representative FKP publicdatabase confirm that the SIFT features obtained after Gabor enhance-ment of the principal finger knuckle lines improve the performance ofthe person identifier.

Introduction: Nowadays, hand recognition has become an importantapplication in modern security systems because of its ease of datacapture, its distinctiveness, its acceptance by the public and its low-level requirement for subject cooperation. Different intra-modalitiesfor hand-biometric approaches have been researched during previousyears such as hand geometry, palmprints and vein patterns. In thisLetter, we focus on finger-knuckle-prints (FKPs). Previous work pro-poses different approaches based on texture or transformed domainsinformation. For instance, [1] and [2] propose texture measures basedon band-limited phase-only correlation and 2D Gabor filters competitivecode obtaining equal error rates (EER) around 1.68 and 1.09%, respect-ively. On the basis of transformed domains, [3] and [4] propose methodssuch as Radon, speeded up robust features (SURF) and probabilisticHough transform (PHT), each of which show promising identificationresults.

The application of scale invariant feature transform (SIFT) as a featureapproach has become a popular method for texture identification. Asproposed in [5], SIFT is invariant to image scaling, rotation, and par-tially invariant to changes in illumination and projective distortion.When applied to hand palm texture, the resulting SIFT features outper-form those methods based purely on texture [6]. However, when appliedto FKP images, as in our experiments, the resulting performance isworse than that obtained with texture measures. We suppose that thisis due to the FKP images being noisier than palm print images so thatthe SIFT features are not able to focus on the discriminative parallelknuckle lines. Our hypothesis is that if the knuckle lines are enhanced,the FKP person identifier device based on SIFT descriptors willimprove.

Therefore, we propose a two-step FKP person identifier system, whichis: 1. to apply Gabor filtering to enhance the knuckle lines and 2. to workout the SIFT descriptors. We proceed as follows.

FKP orientation enhancement: The knuckle lines are enhanced usingthe real 2D Gabor filter defined by:

G(x, y, u, u,w) = 1

2pw2exp − x2 + y2

2w2

{ }

cos{2p(ux cos u) + uy sin u}

where u is the frequency of the sinusoidal wave, u defines the orientationselectivity of the function, and w is the standard deviation of theGaussian envelope. In this Letter we use u = p/2, which orientatesthe Gabor filter so that it is orthogonal to the direction of the knucklelines. Setting w = 2.0 and u = 0.1 enhances the knuckle lines. Greaterrobustness against brightness variation is ensured by turning the discreteGabor filter to average zero.

After the Gabor filtering, a contrast limited adaptive histogram equal-isation (CLAHE) algorithm [7] is applied to improve the knuckle linecontrast.

Orientation enhanced SIFT (OE-SIFT) descriptors: The SIFT algo-rithm is based on selecting several keypoints with similar properties inthe gallery and in the questioned images. Once selected, the numberof common keypoints or matches between both images is the measureof similarity. The keypoint selection and characterisation is conductedin the following three steps [5]:

1. Scale-space extrema detection: The I(x,y) Gabor and equalisedinput FKP image is transformed to:

L(x, y,s) = N (x, y,s) ∗ I(x, y)

ELECTRONICS LETTERS 17th March 2011 Vol. 47

where ∗ corresponds to the convolution operator and N (x, y,s) is aGaussian function with bandwidth s.

2. Keypoint localisation: The keypoints ci = {xi, yi} are obtained byevaluating the maxima and minima of the difference-of-Gaussian func-tion [5]:

D(x, y,s) = L(x, y, ks) − L(x, y,s)

The number of keypoints selected depends on the FKP image. As can beseen in Fig. 1, keypoint location is closely related to the information inthe FKP.

3. Keypoint descriptor: Each keypoint ci is defined by a descriptorvector di which contains the orientations and gradient magnitudesaround the keypoint co-ordinates.

Fig. 1 Left and right: SIFT over greyscale and orthogonal orientationenhanced FKP images, respectively

Verifier: The verifier evaluates the number of matches between a ques-tioned and a gallery FKP image. Let cg

i = {xgi , yg

i }Mi=1 and

cqi = {xq

i , yqi }L

i=1 be the set of gallery and questioned FKP keypoint co-ordinates, respectively. Their descriptors are defined as {dg

i }Mi=1 and

{dqi }L

i=1, respectively. The distance between keypoint descriptors is cal-culated from:

Dd(i, j) = ‖dgi − dq

j ‖2

and the distance between co-ordinates is calculated from:

Dc(i, j) = ‖cgj − cq

j ‖2

where ‖.‖ is the Euclidean norm.We define a match between a gallery ci

g and a questioned cjq keypoint

when Dd(i, j) , 1.3 and Dc(i, j) , 1.1∑M

i=1 ‖cgi − cg

j ‖2/M . Thethresholds 1.3 and 1.1 are worked out heuristically. Nevertheless, ourFKP approach is not particularly sensitive to these values, when theyare in the ranges [1.2 to 1.6] and [1.0 to 1.3], respectively. Thenumber of matches between the questioned and the gallery FKP is thesimilarity score.

Experiments: We have used the Hong Kong Polytechnic FKP database,also used in [8], which can be freely downloaded [9]. This database iscomposed of the segmented FKP images of the left index finger, theleft middle finger, the right index finger and the right middle finger of165 subjects. Each FKP was acquired 12 times during two sessionsgiving a total of 165 × 12 × 4 ¼ 7920 samples. For a fair comparison,we used the same experimental methodology as [8]: the gallery set iscomposed of the six images of the first session while the probe set iscomposed of the remaining six images of the second session. As pro-posed in [8], three experiments were carried out.

1. Experiment 1: All the FKP images are involved. Each image inthe probe set was matched against all the images in the gallery set.Therefore, the number of genuine scores and imposter scores is 23760(165 × 4 × 6 × 6) and 15657840 (165 × 4 × 6 × 164 × 4 × 6),respectively.

2. Experiment 2: This experiment evaluates the performance of theproposed FKP feature extraction method on each type of finger. For eachfinger, the number of genuine scores and imposter scores is 5940 (165 ×6 × 6) and 974160(165 × 6 × 164 × 6), respectively.

3. Experiment 3: This experiment investigates the feature perform-ance when combining information from two or more FKPs of thesame person with a SUM based fusion rule [8].

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Results: Table 1 displays the results of the proposed OE-SIFT approachand the results of [8]. Comparing the results obtained with SIFT and OE-SIFT confirms that the FKP parallel lines enhancement improves theSIFT descriptors and the person identifier scheme. Fig. 2 shows theROC curves comparing both approaches.

0 1 2

FAR, %

3 4 5

100

99

98

97

96

95

GA

A =

1-F

RR

, %

OE-SIFTSIFT

Fig. 2 ROC curves obtained in experiment 1 with OE-SIFT and SIFTapproaches

Table 1: EERs (%) comparison of FKP features obtained with OE-SIFT approaches with results in [8]

[8] SIFT OE-SIFT

Experiment 1

All against all 1.47 2.02 0.85( � 42%)

Experiment 2

Left index 1.73 1.92 1.02( � 41%)

Left middle 1.78 1.93 0.43( � 76%)

Right index 1.44 2.26 0.95( � 34%)

Right middle 1.64 2.14 0.91( � 44%)

Experiment 3

L-index, l-middle 0.20 0.50 0.04( � 80%)

R-index, r-middle 0.26 0.46 0.13( � 50%)

L-index, r-index 0.20 0.36 0.08( � 60%)

L-middle, r-middle 0.27 0.47 0.02( � 92%)

All four 0 0.09 0( � 0%)

Recalling the state-of-the-art results obtained with the same databaseand experimental methodology, the OE-SIFT outperforms the bestresults claimed by [8], with texture based features, reducing the EERby 42%. It also outperforms in experiment 1 the results presented in[10] with BLPOC and CompCodes features with EER equal to 1.66and 1.68%, respectively. Combining both features [10] claim an EERequal to 0.45%. The computation time for OE-SIFT feature extractionand matching is less than 1 second on a Pentium Dual-Core 1.66GHzwith 2Gb RAM.

ELECTRO

Conclusion: A novel feature approach to FKP authentication is pro-posed. It is based on applying the SIFT algorithm to FKP images filteredby a 2D Gabor filter and equalised. The 2D Gabor filter is orientedorthogonally to the knuckle lines to enhance their contrast and toallow SIFT descriptors for FKP based person identification. Resultsobtained provide improvements in the state of the art.

Acknowledgment: This work is partially supported by the SpanishGovernment, under grant MCINN TEC2009-14123-C04-01.

# The Institution of Engineering and Technology 201117 January 2011doi: 10.1049/el.2011.0156One or more of the Figures in this Letter are available in colour online.

A. Morales, C.M. Travieso, M.A. Ferrer and J.B. Alonso (Signals andCommunications Department, University of Las Palmas de GranCanaria, Campus de Universitario Tafira s/n, Las Palmas de GranCanaria E-35017, Spain)

E-mail: ctravieso@dsc.ulpgc.es

References

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