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Experimental evaluation of fingerprint verification system based on double random phase encoding

Hiroyuki Suzuki, Masahiro Yamaguchi, Masuyoshi Yachida, Nagaaki Ohyama, Hideaki Tashima, and Takashi Obi

Open Access Open Access

Abstract

We proposed a smart card holder authentication system that combines fingerprint verification with PIN verification by applying a double random phase encoding scheme. In this system, the probability of accurate verification of an authorized individual reduces when the fingerprint is shifted significantly. In this paper, a review of the proposed system is presented and preprocessing for improving the false rejection rate is proposed. In the proposed method, the position difference between two fingerprint images is estimated by using an optimized template for core detection. When the estimated difference exceeds the permissible level, the user inputs the fingerprint again. The effectiveness of the proposed method is confirmed by a computational experiment; its results show that the false rejection rate is improved.

©2006 Optical Society of America

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Figures (10)
Fig. 1.
Fig. 1. Smart card holder authentication system which combines fingerprint verification with PIN verification by applying a double random phase encoding.

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Fig. 2.
Fig. 2. Flow of double random phase encoding.

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Fig. 3.
Fig. 3. The POC waveforms are shown. The left waveform is obtained by calculating the POC between same fingerprints, and the right one is the POC between different fingerprints.

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Fig. 4.
Fig. 4. (a) The method of bit encoding into 2D image is shown. When the right square is white, these squares express zero, and when the left one is white, they express one. (b) An example image which is transformed from a character sequence “1234ABCD”. Tags are also installed at the upper left corner and the lower right corner of the bit pattern.

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Fig. 5.
Fig. 5. Procedure of decoding a PIN data from the decrypted image.

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Fig. 6.
Fig. 6. Workflow of the preprocessing.

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Fig. 7.
Fig. 7. This figure shows the template image which is a part of an ellipse. The parameters which represent the characteristic of the arc are also shown.

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Fig. 8.
Fig. 8. These images show the experimental result of template image generation, (a) optimized template image, (b) estimated cores, which are represented by black points.

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Fig. 9.
Fig. 9. Result images are shown, (a) fingerprint image for enrollment, (b) encryption key image generated from (a), (c) original PIN image, (d) same individual’s fingerprint image for verification, (e) decryption key image generated from (d), (f) decrypted image by using (e), (g) different individual’s fingerprint image for verification, (h) decryption key image generated from (g), (i) decrypted image by using (h).

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Fig. 10.
Fig. 10. The actual position differences between two fingerprints used in the experiment are plotted. These graphs also show the result of verification in the case of (a) without removing (b) with removing the significantly shifted fingerprints.

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Tables (1)

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Table 1. Accuracy of experimental verification

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Equations (17)

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f m ( x , y ) = f ( x , y ) exp ⁡ [ jR ( x , y ) ] .
f r ( x d , y d ) = ℑ ⌊ F m * ( u , v ) N ( u , v ) ⌋
= f m * ( x d , y d ) * n ( x d , y d ) ,
g 1 ( x ′ , y ′ ) = ℑ − 1 [ exp ⁡ { j K E ( u , v ) } ] ,
g 2 ( x ′ , y ′ ) = ℑ − 1 [ exp ⁡ { j K D ( u , v ) } ] .
G E ( u , v ) = ℑ [ g E ( x ′ , y ′ ) ]
= A E ( u , v ) exp ⁡ { j P E ( u , v ) } ,
G D ( u , v ) = ℑ [ g D ( x ′ , y ′ ) ]
= A D ( u , v ) exp ⁡ { j P D ( u , v ) } ,
K E ( u , v ) = P E ( u , v ) ,
K D ( u , v ) = P D ( u , v ) .
n ( x d , y d ) ≅ { δ ( x d − α , y d − β ) ( correct fingerprint ) random sequence ( incorrect fingerprint ) ,
( x e , y e ) = ( x 1 − x 2 , y 1 − y 2 ) ,
Δ E = average ∣ ( x a − x e ) 2 + ( y a − y e ) 2 ∣ all training image pairs ,
BER = N Error N PIN ,
FRR = N FP N S ,
FAR = N FN N S ,
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