Optical security system for the protection of personal identification information

Optical security system for the protection ofpersonal identification information

Yang-Hoi Doh, Jong-Soo Yoon, Kyung-Hyun Choi, and Mohammad S. Alam

A new optical security system for the protection of personal identification information is proposed. First,authentication of the encrypted personal information is carried out by primary recognition of a personalidentification number (PIN) with the proposed multiplexed minimum average correlation energy phase-encrypted (MMACE_p) filter. The MMACE_p filter, synthesized with phase-encrypted training images,can increase the discrimination capability and prevent the leak of personal identification information.After the PIN is recognized, speedy authentication of personal information can be achieved throughone-to-one optical correlation by means of the optical wavelet filter. The possibility of informationcounterfeiting can be significantly decreased with the double-identification process. Simulation resultsdemonstrate the effectiveness of the proposed technique. © 2005 Optical Society of America

OCIS codes: 100.0100, 070.5010, 070.4560, 100.7410.

1. Introduction

With the development of an information societycomes a tremendous increase in the use of a variety ofidentification cards such as passports, driver’s li-censes, passes, and credit cards. However, the devel-opment of reproduction technology has madeidentification and credit card information more sus-ceptible to counterfeiting, which is becoming a seri-ous social problem. Also, personal credit informationcan be falsified through theft of personal identifica-tion numbers (PINs) and credit card numbers. As aresult, anticounterfeit measures are urgently needed.Holograms are embedded in credit cards and identi-fication cards in an effort to solve this problem. How-ever, they cannot prohibit fraud owing to theexistence of optical intensity detectors that make itpossible to reproduce and synthesize master holo-grams. Research into new ways of preventing cardcounterfeiting and fraud in any situation and of tak-

ing advantage of simple applications of optics is on-going.1,2

Existing optical image encryption methods use re-covered images to authenticate personal identifica-tion information.3–5 For example, recovered picturesof a face and the face of the person who presents anidentification card are compared with the naked eye,the recovered face images are compared with the per-son’s face images saved in the database, or the recov-ered PIN image is used to compare with the face ofthe person who presents the identification card. But,in the case of recovering PINs, the numbers can beleaked during the recovery process. So it is not ap-propriate to use PINs in cases such as credit card andconfidential authorizations that require securitycodes. Therefore, during recovery of the encryptedimage, the personal identification information shouldnot be visible to the naked eye. Also, authentication ofthe personal identification information only in therecognition system, without the recovery process ofthe encrypted image, is needed.

In this paper a new optical security system is pro-posed to protect personal identification information.Encryption of personal identification information iscarried out by use of a random-phase pattern in thespatial plane and the Fourier plane that is based onthe double random-phase encryption method sug-gested by Towghi et al.5 Authentication of encryptedidentification is carried out primarily through theverification of personal identification information bythe classification and recognition of PINs with theproposed multiplexed minimum average correlation

Y.-H. Doh ([email protected]) is with the Department of Elec-trical and Electronics Engineering, Cheju National University,Jeju 690-756, South Korea. J.-S. Yoon is with the SK Tech Com-pany, Gyunggi 472-905, South Korea. K.-H. Choi is with the De-partment of Mechanical Engineering, Cheju National University,Jeju 690-756, South Korea. M. S. Alam is with the Department ofElectrical and Computer Engineering, University of South Ala-bama, Mobile, Alabama 36688-0002.

Received 20 May 2004; revised manuscript received 16 October2004; accepted 18 October 2004.

0003-6935/05/050742-09$15.00/0© 2005 Optical Society of America

742 APPLIED OPTICS � Vol. 44, No. 5 � 10 February 2005

energy phase-encrypted (MMACE_p) filter. The PINcan be recognized in the form of encryption withoutrecovery, thus preventing the leak of personal iden-tification information. After recognition of the PIN,authentication of the personal identification card isdetermined through a one-to-one optical correlationbetween the recovered face image and the person’sface information saved in the database by use of anoptical wavelet matched filter (OWMF).6 Use of theOWMF can increase the authentication speed andmarkedly decrease the possibility of fraud. Thus theproposed system is expected to provide the benefits ofsecurity and speed to the processing of financial andcredit transactions as well as the achievement of se-curity control.

2. Encryption of Personal Identification Information

A. Phase Encryption of Face Images

Encrypted face images should be recovered and com-pared with the face of the person who presents anidentification card. In this paper the double random-phase encryption method5 suggested by Towghi et al.is used for this process. This method is based on a 4foptical correlator, which can double encrypt in thespatial and the Fourier planes. Figure 1 illustratesthe optical setup used for the encryption process [Fig.1(a)] and the decryption process [Fig. 1(b)]. In thespatial plane a normalized input image is phase en-coded, and the phase-encoded image is multiplied bya random-phase pattern to be encrypted. In the Fou-rier plane the Fourier-transformed information ismultiplied by another random-phase pattern, result-ing in double encryption.

If we let f�x, y� denote the primary normalized in-put image to be encrypted, the phase-encoded input

image c�x, y� is expressed as follows:

c(x, y) � exp[j�f(x, y)]. (1)

Here the range of variation in the phase encoding is�0, ��. The random-phase patterns, g�x, y� andQ�u, v�, which are used in the spatial plane and theFourier plane, respectively, are obtained by transfor-mation of two random sequences, p�x, y� and b�u, v�,uniformly distributed in �0, 1�, as follows:

g(x, y) � exp�j2�p(x, y)�,

Q(u, v) � exp�j2�b(u, v)�. (2)

What follows is a description of the phase encryp-tion process for the input image f�x, y�. First, thephase-encoded input image c�x, y� is multiplied by therandom-phase pattern g�x, y� in the spatial plane.Then the Fourier-transformed information is multi-plied by the random-phase pattern Q�u, v� in the Fou-rier plane. Finally, the encrypted image �f�x, y� isobtained by taking an inverse Fourier transform ofthe result of the Fourier plane. This process is givenby the following equation:

�f(x, y) � ��1{�[c(x, y)g(x, y)]Q(u, v)}� [c(x, y)g(x, y)] � q(x, y)� {exp[j�f(x, y)]exp[j2�p(x, y)]} � q(x, y),

(3)

where �� is the Fourier-transform operator, ��1� � isthe inverse Fourier-transform operator, R is the con-volution operator, and q�x, y� is the impulse responseof Q�u, v�.

The decryption process is the reverse of the encryp-tion process. First, the encrypted image �f�x, y� isFourier transformed and multiplied by the complexconjugate of Q�u, v� in the Fourier plane. Then thephase-encoded input image c�x, y� is obtained by mul-tiplying the complex conjugate of g�x, y� in the spatialplane. The following equation describes this process:

c(x, y) � (��1{�[�f(x, y)]Q*(u, v)})g*(x, y)� [c(x, y)g(x, y)]g*(x, y)� c(x, y)� exp[j�f(x, y)]. (4)

The original input image f�x, y� is recovered by extract-ing the phase of c�x, y�, and dividing it by �. That is,the random phase pattern g�x, y� in the spatial plane,and Q�u, v� in the Fourier plane must be known torecover the encrypted image. Therefore they are essen-tial to the decryption of the encrypted image.

B. Phase Encryption of a Personal Identification Number

For cases in which security is required and secretcodes and personal identification information shouldnot be shown, the proposed encryption method of a

Fig. 1. Optical implementation of (a) the phase encryption pro-cess and (b) the decryption process.

10 February 2005 � Vol. 44, No. 5 � APPLIED OPTICS 743

PIN can be used for authentication of personal iden-tification without recovery of the encrypted identifi-cation information. Let fi�x, y� denote each numberimage constituting the PIN and pi�x, y� denote theindependent white sequence corresponding to eachnumber image. Then each phase-encrypted trainingimage ti�x, y� can be defined as follows:

ti(x, y) � exp[j�fi(x, y)]exp[j2�pi(x, y)], (5)

i � 0, 1, . . . , 9.

Each encrypted number image constitutes the en-crypted PIN image t�x, y� in the spatial plane. Theencrypted PIN image in the spatial plane t�x, y� isdouble encrypted again by the random-phase patternQ�u, v� � exp�j2�b�u, v�� in the Fourier plane. Thefollowing is the equation for �t�x, y�, the double-encrypted PIN image:

�t(x, y) � ��1�T(u, v)Q(u, v)�

� t(x, y) � q(x, y), (6)

where T�u, v� denotes the Fourier transform of theencrypted image t�x, y�.

Phase encryption of the PIN image can be imple-mented optically by use of the 4f optical correlatorsystem shown in Fig. 1(a). The spatially encryptedPIN image t�x, y� is placed in the input plane of theoptical correlator. The PIN image is Fourier trans-formed and then multiplied by the random-phasepattern Q�u, v� � exp�j2�b�u, v�� in the Fourierplane. The multiplied result in the Fourier plane isthen inverse Fourier transformed. Then the double-encrypted PIN image �t�x, y� can be obtained as aresult of convolution of t�x, y� and q�x, y� in the outputplane.

The decryption key of the Fourier plane, Q*�u, v�� exp��j2�b�u, v��, is used to recognize the spatiallyencrypted PIN according to a filter design with thetraining image of the number image, ti�x, y�, en-crypted in the spatial plane. The training image gen-erated by the phase encryption of number images inthe spatial plane can increase discrimination capa-bility by decreasing the similarities among trainingimages containing more information than simplenumber images. Also, all the random-phase patternsused for the encryption process must be known inorder to recognize the PIN from the encrypted image.Therefore these patterns can be used effectively toprotect secret codes and personal information.

3. Authentication of Personal Identification Information

A. Classification and Recognition of a PersonalIdentification Number

In the proposed method for authentication of per-sonal identification information, a PIN is first classi-fied and recognized for personal identification. TheMMACE_p filter proposed to classify and recognize a

PIN is made by synthesizing the phase-encryptedtraining images with the minimum average correla-tion energy (MACE) filter7 and by multiplexing. Thedesign of the MMACE_p filter is easily approached byunderstanding the design process of the multiplexedMACE (MMACE) filter.8 Comparison of the perfor-mance of the MMACE_p filter with that of theMMACE filter is recommended.

The MMACE filter uses the multiplexing methodwith modulation of the MACE filters in the Fourierplane. In this paper four MACE filters are multi-plexed to effectively classify and recognize the num-bers from 0 to 9 (0–9). The multiplexed correlationresults are changed into codes and are demonstratedin four subplanes. The four MACE filters used forrecognition of PINs are as follows:

HMACE, i � D�1F�F�D�1F��1ui, i � 1, 2, 3, 4. (7)

Here the � sign denotes the complex conjugate trans-pose transformation, the matrix D indicates the av-erage energy spectrum of number images 0–9, andthe row vector F shows the Fourier transformedtraining images (number images 0–9):

F � �F0 F1 . . . F9�. (8)

The constraint vector ui can restrict the correlationpeak values in each subplane to the required rate bycontrolling the vector elements. For training imagesto be recognized in each subplane, the element 1 isplaced in order to show the maximum correlationpeak; however, for unrecognized training images, theelement 0 is placed so as not to show the correlationpeak. With this code, the training images (numberimages 0–9) of the recognition objects can be matchedto specific codes. Thus, with the exception of the all-code value of 0, any code value can be used and amaximum of 15 different codes can be used. The rea-sons for excluding the all-code value of 0 are as fol-lows: Although the code can be generated with avalue of 0, this introduces the possibility of misiden-tification when image information does not exist inthe input plane or when the numbers and charactersthat were not included in the training images areinput. Table 1 shows the codes used in this paper torecognize PINs generated by the combination of num-ber images 0–9. The following is the correspondingconstraint vector u:

Table 1. Codes for the Classification and Recognition of PINs

Subplane

Image Number

0 1 2 3 4 5 6 7 8 9

Sub-P1 1 0 0 0 0 0 0 0 1 1Sub-P2 1 0 0 0 1 1 1 1 0 0Sub-P3 0 0 1 1 0 0 1 1 0 0Sub-P4 0 1 0 1 0 1 0 1 0 1


u1 � �1 0 0 0 0 0 0 0 1 1�,

u2 � �1 0 0 0 1 1 1 1 0 0�,

u3 � �0 0 1 1 0 0 1 1 0 0�,

u4 � �0 1 0 1 0 1 0 1 0 1�. (9)

For example, when number 6 is input, a correlationpeak is not shown in subplane 1 by the constraintvector u1, resulting in the generation of code 0. Theconstraint vector u2 generates code 1 by showing themaximum correlation peak in subplane 2. In thesame way, constraint vectors u3 and u4 in subplanes3 and 4, respectively, generate codes 1 and 0, respec-tively. Thus number 6 is recognized by code 0110.

The following equation shows the MMACE filterafter multiplexing of the four MACE filters in theFourier plane:

HMMACE(u, v) � �i�1

i�4

H*MACE, i(u, v)exp[�j2�(miu

� niv)], (10)

where mi and ni are spatial-shift coefficients that pre-vent the four correlation results from overlapping oneanother in the output correlation plane. The coeffi-cients show, respectively, the horizontal and the ver-tical shifts of the correlation results. Consequently,the correlation results of the PIN images with theMMACE filter are divided into four independent sub-planes. After thresholding, PINs are recognized bycomparison with the codes in Table 1.

MMACE_p filters can also classify and recognizeencrypted PIN images without the use of a recoveryprocess. Synthesis of the MMACE_p filter beginswith synthesis of the phase-encrypted training im-ages ti�x, y�, instead of the number images with theMACE method in the spatial plane. The MACE_pfilter for recognition of the phase-encrypted images iswritten as follows:

HMACE_p, i � DT�1T�T�DT

�1T��1ui. (11)

Here the matrix DT denotes the average energy spec-trum of the phase-encrypted training images in thespatial plane and the row vector T is the Fouriertransform of the encrypted training images. The con-straint vector ui is the same as in the Eq. (9), and thecode table for the classification and recognition ofPINs is established in the same way as shown inTable 1. The MMACE_p filter synthesized by multi-plexing MACE_p filters is written as follows:

HMMACE_p(u, v) � �i�1

i�4

HMACE_p, i* (u, v)

� exp[�j2�(miu � niv)]Q*(u, v).

(12)

Because the MMACE_p filter should recognize en-crypted images without the use of a recovery pro-cess, decryption key information must be includedin the filter. Here the decryption key, Q*�u, v�� exp��j2�b�u, v��, is a complex conjugate of therandom-phase pattern that is used in the encryptionprocess of the Fourier plane.

The optical system for recognition of PINs uses the4f optical correlator shown in Fig. 1. PINs can berecognized through the following steps: The en-crypted PIN image is placed in the input plane, theproposed MMACE_p filter is placed in the Fourierplane, and then the coded correlation distribution isobtained in the output plane.

B. Face Recognition

The personal authentication process is establishedafter personal identification is verified by the classi-fication and recognition of PINs with the MMACE_pfilter. The recognition system for personal authenti-cation requires outstanding discrimination capabilityfor recognizing similar faces even in a noisy environ-ment. Wavelet transform is effective in the featureextraction of images. Therefore the features of faceimages are extracted by use of wavelet transform,and the extracted face information is saved in thedatabase in the form of an OWMF. Then the authen-ticity of the personal information is verified by one-to-one optical correlation of the recovered face imageand the face information in the database.

If the face image is f�x, y�, then OWMF is given asfollows:

Wf(u, v) � F(u, v)*|Ha(u, v)|2. (13)

Here Ha�u, v� is a Fourier-transformed wavelet func-tion and a Mexican-hat wavelet function, defined asfollows:

Ha(u, v) � 4�2a2(u2 � v2)exp[�2�2a2(u2 � v2)].(14)

Here a is scaling parameter, and its value should beselected according to the features and uses of theimages. In most cases wavelet-transformed imageshave emphasized edge information. The effect of edgeemphasis varies according to the value of the scalingparameter and the type of wavelet function. There-fore each person’s facial features can be extracted byuse of wavelet transform, and the effects of noise canbe minimized.

The optical system for the recognition of face im-ages also uses the 4f optical correlator shown in Fig.1. If the recovered face image is placed in the inputplane and the suggested OWMF is placed in the Fou-rier plane, a correlation distribution can be obtainedin the output plane and be used to recognize the faceimage.


4. Simulation Results

Various types of identification information, such as apicture of a person’s face, a fingerprint, a PIN, asignature, and a seal, are attached to identificationcards. For this paper, a picture of a face (96 � 128pixels, gray-scale image) and a PIN (220 � 32 pixels,binary image) are used as personal identification im-ages. The proposed optical security system authenti-cates the person’s identification by recognizing thePIN image and by comparing the face informationwith the recovered face image.

A. Classification and Recognition of a PersonalIdentification Number

The proposed MMACE_p filter is used for the classi-fication and recognition of a PIN to show the effec-tiveness of the proposed method by comparing it withthe MMACE filter. Figure 2 shows that the binaryPIN image without noise can be classified and recog-nized by use of the MMACE filter and the MMACE_p

filter. Figure 2(a) is the recovered image of the en-crypted PIN, and it contains all the numbers from 0to 9. The recovered image shows the same image asthe input image because noise is absent. Figure 2(b)is the encrypted PIN image in the spatial plane, andits phase pattern is described in a 256 gray-scaleimage for convenience. Figure 2(c) shows the corre-lation result of the recovered image of Fig. 2(a) andthe MMACE filter. Each subplane is presented so asnot to overlap one another in the 440 � 64 pixelplane. Figure 2(d) is the correlation result of the en-crypted image of Fig. 2(b) and the proposed MMA-CE_p filter, which shows greater recognitioncapability and less noise than the MMACE filter.

The recognition capability of a filter can be evalu-ated by the signal-to-noise ratio (SNR), defined asfollows:

SNR � 10 logrmax

Nrms(dB), (15)

Fig. 2. Recognition of a PIN without noise: (a) recovered image (the same as the input image), (b) encrypted image, (c) correlation resultbetween the recovered image and the MMACE filter, (d) correlation result between the encrypted image and the MMACE_p filter, (e) resultof thresholding of (c), and (f) result of thresholding of (d).


where rmax denotes the maximum correlation-peakvalue in the correlation plane and Nrms is the effectivevalue of the values less than 50% of the maximumcorrelation-peak value. When large, sharp correla-tion peaks with small sidelobes appear in the corre-lation plane, the SNR is a large value. In the case ofsmall, broad correlation peaks with large sidelobes,the SNR is a small value. The SNR of Fig. 2(c) is19.6 dB, and the SNR of Fig. 2(d) is 21.4 dB. Thisshows the higher effectiveness of the MMACE_p fil-ter compared with the MMACE filter.

Figures 2(e) and 2(f) show four thresholded sub-planes. All the values that are larger than the thresh-old are assigned a value of 1. The postthresholdingcorrelation results of Figs. 2(c) and 2(d) at 50% of thecorrelation-peak value are shown as white dots. Thevalues from the same location in each subplane arecompared with the codes in Table 1, and then thePINs are precisely classified and recognized. As anexample, the locations identified by four white circlesare examined. The two filters obtain the code value0110 from each subplane, and then the code value is

compared with the code table. Finally, the number 6is recognized as a result of the comparison. In thesame way, other numbers in other locations are alsorecognized without error. In other words, when theencrypted PIN image has no noise, the SNR in thecase of the MMACE_p filter is higher than in the caseof the MMACE filter, which performs excellently.However, the difference between the two filters can-not be determined in the case of thresholding, be-cause the same output is generated by both filters.

Figure 3 shows that when Gaussian white noise isadded to the encrypted image, the MMACE_p filterperforms excellently. Figures 3(a) and 3(b) show therecovered image and the encrypted image, respec-tively, in the presence of Gaussian white noise with azero mean and a standard deviation of � � 1. Therecovered image shows that a considerable error wasgenerated. The mean-squared error (MSE) is used asa metric to compare the recovered images with theoriginal image. Let f�x, y� denote the original imageand fr��x, y� the recovered image. The MSE is definedas follows5:

Fig. 3. Recognition of a PIN in the presence of zero-mean Gaussian white noise with a standard deviation of 1: (a) recovered image, (b)encrypted image, (c) correlation result between the recovered image and the MMACE filter, (d) correlation result between the encryptedimage and the MMACE_p filter, (e) result of thresholding of (c), and (f) result of thresholding of (d).


MSE(|fr�|) � E 1N � M �

x�1

x�N

�y�1

y�M

[�f(x, y)|

� |fr�(x, y)�2], (16)

where N � M is the number of pixels of the original

image and E� � is the expected value. In Fig. 3 theMSE is found to be 0.0756. Figure 3(c) is the corre-lation result obtained by the MMACE filter, and itshows a decrease in correlation-peak values and anincrease in noise. On the other hand, the correlationresult obtained by the proposed MMACE_p filter [seeFig. 3(d)] shows near-uniform correlation-peak val-ues and less noise compared with the results shownin Fig. 3(c).

To classify and recognize PINs, we examine Figs.3(e) and 3(f), which threshold the correlation resultsof Figs. 3(c) and 3(d), respectively. Then the disparityof the result of subplane 4 can be identified. Althoughthe MMACE_p filter shown in Fig. 3(f) generates anaccurate code of 11111 00000, in subplane 4 theMMACE filter shown in Fig. 3(e) generates an inac-curate code of 10110 00000. In other words, in theMMACE filter the correlation-peak values are low-ered by the influence of the noise, and the elementthat should be recognized as code 1 is misidentified ascode 0. Therefore the MMACE filter misidentifies

Fig. 4. SNR variations of the correlation output versus thechanges in the standard deviation of noise.

Fig. 5. Face images for personal authentication: (a) reference and test images, (b) wavelet-transformed reference image and wavelet-transformed test images, (c) recovered images of the noisy reference images.


number 3 as 2 and number 9 as 8, but the MMACE_pfilter recognizes all the numbers correctly.

Figure 4 shows the SNR variations in the correla-tion output against the changes in the standard de-viation of noise. As the standard deviation of noiseincreases, the SNR of both the MMACE and MMA-CE_p filters decreases; however, compared with theMMACE filter, the proposed MMACE_p filter has ahigher SNR, showing its excellent performance.When PINs are classified and recognized with theMMACE filter, misidentification occurs from a stan-dard deviation of noise of � � 0.85, whereas the pro-posed MMACE_p filter generates misidentificationfrom a standard deviation of noise of � � 1.75. This isbecause much more information is contained in theMMACE_p filter, which synthesizes phase-encryptednumber images, compared with the MMACE filter,which synthesizes simple number images. Thereforerecognition of identification information without useof the recovery process is considered more effective.

B. Personal Authentication

Personal authentication is implemented through thecorrelation between the personal face information,identified primarily by the recognition of the PIN,and the recovered face image. Figure 5(a) shows theface images used for personal authentication: refer-ence image, test image 1, test image 2, test image 3,and test image 4, as labeled. Figure 5(b) shows theimpulse response of the same face images saved inthe database in the form of an OWMF, which is theresult of the wavelet transform of the face images inFig. 5(a). The edge information from the face imagesis identified and emphasized with the scaling param-eter a � 1. Figure 5(c) shows the recovered images ofthe noisy reference images in order to verify the pos-

sibility of authentication of the image, which is pol-luted with noise. The left image �� 0� is withoutnoise. The rest are encrypted noisy images with stan-dard deviations of 0.3, 0.5, 0.7, and 1. The MSEsgenerated in the recovered images are 0.0043, 0.0126,0.0269, and 0.0520, respectively.

Figure 6 shows the variations in the correlation-peak value according to the amount of Gaussianwhite noise added to the reference image. Correlationis performed between the recovered reference imageswith noise and the various face-image information(reference image and test images 1–4) saved in thedatabase. The correlation-peak values are normal-ized based on the correlation-peak value between therecovered reference image without noise and the ref-erence image information saved in the database. Ahigh correlation-peak value between the recoverednoisy reference images and the saved reference imagemeans that there is a high probability of accuraterecognition of the recovered basic images. On theother hand, a high correlation-peak value betweenthe recovered reference images and the saved testimages means that there is a high probability of mis-identification. The minimum correlation-peak valuefor the accurate recognition of noisy reference imagesis set at 70% for the case without noise, whereas thecorrelation-peak value for preventing the misidenti-fication of test images is set at 30%. This margin of40% can increase the confidence of the authentica-tion. For the face images used in this paper, when thestandard deviation of Gaussian white noise is lessthan 0.85, which satisfies the above condition, securerecognition can be carried out without misidentifica-tion.

5. Conclusion

In this paper a new optical security system is pro-posed to protect an individual’s personal identifica-tion information. The proposed system implementsdouble encryption of a personal information imageconsisting of a PIN and a face image by use ofrandom-phase patterns in the spatial plane and theFourier plane of a 4f correlator. Authentication of theencrypted personal identification card is carried outby primary classification and recognition of the PINwith the MMACE_p filter. The MMACE_p filter, syn-thesized with the phase-encrypted training image inthe spatial plane, can increase the discrimination ca-pability compared with the MMACE filter, which issynthesized with a simple number image. Also, theleak of personal identification information can be pre-vented because recognition is implemented under en-crypted conditions without number recovery. Thenthe authenticity of the personal identification card isdetermined through one-to-one optical correlation be-tween the recovered face image and the face imagessaved in the database in the form of an OWMF. One-to-one correlation can increase the authenticationspeed. Also, the possibility of fraud can be decreasedwith the double-identification process. Therefore it is

Fig. 6. Variations in the correlation-peak value according to theamount of Gaussian white noise added to the reference image.


expected that the proposed system will provide theadvantages of secure and speedy processing of a widevariety of financial and credit transactions as well asa means of security control.

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Optical security system for the protection of personal identification information

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