Разработки и технологии в области защитных голограмм

Transcription

1 Разработки и технологии в области защитных голограмм SECURITY HOLOGRAM MASTER-MATRIX AUTOMATIC QUALITY INSPECTION BASED ON SURFACE RELIEF MICRO-PHOTOGRAPHS DIGITAL PROCESSING Zlokazov E., Shaulskiy D., Starikov R., Odinokov S., Zherdev A., Koluchkin V., Shvetsov I., Smirnov A. National Research Nuclear University MEPhI Bauman Moscow State Technical University Krypten Research and Production Company Introduction Application of security holograms (SH) for document, product or authority protection is used widely around the globe. High security level of SH is achieved by unique design of holographic images that combine complex 3D scenes with such a security features as concealed images and microtexts, kinetic effects, hidden laser readable images etc. Mass production of SH utilizes widespread technique of hot foil or lavsan paper stamping with the use of nickel mastermatrix. The relief of master-matrix represents pixelated structure of diffraction patterns with the grating period of up to 0.1μm. Riffling of such a structure requires application of specialized and technologically advanced lithography equipment. Thus, the forgery of SH is next to impossible. The quality of mass produced SH is basically depends on ideality of master-matrix that is used in pressing equipment. Defects and relief distortions caused by elastic deformation during copying, excessive ware and mechanical damage of mastermatrix lead to degradation of holographic image and loss of its unique security features. Thus the problem of prior master-matrix quality inspection causes a special interest of SH mass manufacturers. In this paper we represent the method of automatic SH master-matrix quality inspection based on digital processing of its surface relief dark-field 55

2 microphotographs. Our method is based on retrieving of special 2D samples from Fourier specters of these microphotographs and providing a correlation matching of these samples with template sample. This template sample was preliminary synthesized according to distortion-invariant correlation filter with maximum average correlation height (MACH-filter) calculation algorithm. 1. Method summary Figure 1 represents two dark-field microphotographs of same surface region taken from different master-matrices, which were expertly rated as good - quality and bad -quality. These images were captured by the use of Carl Zeiss microscope with 500x zoom. The wear and damage of master-matrix cause distortions and lowering of contrast in diffraction grating representation. These changes can be automatically detected using Fourier domain analysis. Fig. 1. Dark-field microphotographs of different quality nickel master-matrices surfaces: a) good -quality; b) bad - quality 1.1 Fourier analysis of master-matrix surface microphotographs Fourier frequency distribution of periodical structures such that represented on figure 1 contain sharp DC-term on the center of Fourier plane and a pair of equidistant ±1 peaks for each type of grating represented on input image. The distance between peaks and DC-term depends on frequency of corresponding grating. Shift direction of ±1 peaks depends on corresponding grating orientation. Peaks amplitude mainly depends on the size of occupied by corresponding grating part of input image and grating contrast. 56

3 Fourier spectrum calculations showed that distortions and wear of master-matrix surface cause significant changes in Fourier spectrum of its dark-field microphptography. In the main these changes concentrate in the areas around sidelobe peaks of the spectrum. The amplitude of peaks decreases and some of the sidelobe peaks may disappear. The value of these distortions directly depends on quality of input master-matrix surface. The main idea of our method is to provide a correlation matching between spectral sample of master-matrix to be tested and template sample, which was preliminary synthesized on the basis of similar spectral samples of high-quality training master-matrices surface images [1]. Fig. 2. Amplitude image of Fourier spectrum of microphotograph from figure 1(a) with suppressed DC-term; enlarged sample is the area of +1-order peak 1.2 Correlation matching of spectral samples The method of correlation image matching is based on calculation of 2D cross-correlation function between input image and template target sample. Sharp and high peak in the center of cross-correlation function means that object represented in input image belongs to the target object class. According to its definition and Fourier transform cross-correlation theorem a correlation function can be calculated as: where sin input image, Sin input image Fourier transform, st template sample, St template sample Fourier transform, F -1 [ ] denotes backward Fourier 57

4 transform operation, ( ) denotes termwise multiplication of matrices and ( ) denotes complex conjugation. Inspection of different quality samples showed that amplitude variations inside +1 peak area of microphotographs 2D-Fourier spectrum in some cases cause significant degradation of cross-correlation peak between samples of same class. These variations caused by unique features of each master-matrix and errors in camera positioning during the micro-photographing. To avoid this problem the statistical data about spectral samples of good -class images should be considered during the template sample selection. In our case we used correlation filter MACH synthesis algorithm that uses a set of training images of target-class object (spectral samples of good - quality master-matrices in our case) [2,3]. Selection of training images distorted in a priori predetermined range allows adjustment of filter recognition invariance. If the target object image varies in the preassigned distortion limits, MACH-filter will give a positive correlation response. We used only good -type spectral samples in the training set. Correlation filters was calculated in Fourier spectral domain according to: H = S 1G, (2) where H vector of MACH-filter Fourier transform coefficients, G vector of mean values of training samples Fourier transform elements, Sg diagonal matrix, which diagonal elements equal to dispersion of training samples Fourier transform elements. 2. Method application for quality inspection of real master-matrices The spectral correlation analysis method represented in prior section was experimentally tested with the use of real SH master-matrices. The quality of all the test master-matrices was preliminary verified by experts. For experimental modeling we used 6 good and 6 bad -quality examples. According to our method the same surface element of all test samples was chosen for inspection. We intentionally used manual positioning of each master-matrix thus collecting positioning errors in the training samples. For each master-matrix we made 4 microphotographs with (5μm,5μm), (-5μm, 5μm), (-5μm, -5μm) and (5μm, - 58

5 5μm) shifts from the center of the selected surface area and 4 microphotographs with (10μm,10μm), (-10μm, 10μm), (-10μm, -10μm) and (10μm, -10μm) shifts. The resolution of microphotographs was pixels. The size of +1-peak spectral samples to be used in correlation matching was pixels. The principal task of the experiment was the investigation of MACH-filter discrimination capability in dependence of amount and type of training set images selected for filter synthesis. We used absolute amplitude of correlation peak as correlation metric. The estimation of synthesized filter discrimination accuracy was provided by calculation of threshold level and false alarm error P0 according to Neyman Pearson criterion. Calculations showed that better accuracy can be achieved if the filter training set includes surface images with maximum positioning error. The achieved false alarm probability P0 in case of only 10μmshifted images in training set was 0.79%. For comparison, in case of only 5μm shifted images in training set P0 was about 1.27%. In case when both 5μmshifted and 10μm-shifted images was added in the training set the false alarm error was P0 = 0.27%. Fig. 3. Recognition capability of our method in case of both 5μm-shifted and 10μm-shifted images in the training set for MACH-filter synthesis 59

6 Conclusion In this paper authors represented the results of distortion-invariant correlation filters application for the real technical task of security hologram master-matrices automatic quality inspection. Depreciation and damages of master-matrix cause lowering and distortion of its surface grating. Analysis of surface dark-field micropfotographs showed that master-matrix distortions affects localized regions in microphotograph spectrum. These regions can be used as characteristic sample of input master-matrix for correlation matching. Application of MACH- filter design algorithm for template sample synthesis allowed to achieve invariance of the method to positioning distortions and local variabilities of real master-matrices. The minimum of false alarm error achieved during method application for test samples of real SH master-matrices was P0 = 0.27%. Acknowledgments Represented work was supported by Krypten Research and Production company, Moscow, Russian Federation. References 1. Vijaya Kumar, B. V. K., Mahalanobis, A., and Juday, R. D., [Correlation Pattern Recognition], Cambridge University Press, New York (April 2005). 2. Abhijit Mahalanobis, Vijaya Kumar, B. V. K., Song, S., Sims, S. R. F., and Epperson, J. F., Unconstrained correlation filters, Applied Optics 33(17), (1994). 3. Kerekes, R. A. and Vijaya Kumar, B. V. K., Selecting a composite correlation filter design: a survey and comparative study, Optical Engineering 47(6), (2008). 4. Proc. of SPIE Vol P-4 60