Lossless Compression of Biometric Images and Digital Watermarking with IDP Decomposition ROUMEN KOUNTCHEV Radiocommunications Dept. Technical University of Sofia Bul. Kl. Ohridsky, 8, Sofia 1000 BULGARIA http://www.tu-sofia.bg
[email protected]
MARIOFANNA MILANOVA VLADIMIR TODOROV Computer Science Dept. ROUMIANA KOUNTCHEVA University of Arkansas T&K Engineering at Little Rock Mladost 3, POB 12 2801 S. University Ave. Sofia 1712 AR, 72204, USA BULGARIA http://www.ualr.edu
[email protected] [email protected] [email protected]
Abstract: In the paper is presented a new method for efficient lossless compression of basic biometric information (fingerprints and signature images), which to be used for digital watermarking of multimedia signals. The image compression is based on two-level Inverse Difference Pyramid (IDP) Decomposition with 2D Walsh-Hadamard Transform, followed by Histogram-Adaptive Run-Length data coding. The compressed biometric information is later used as a watermark, which is embedded in the processed data, or is inserted as an additional level in the IDP decomposition, when an image or multimedia data is processed. In the paper are presented the comparison results, obtained with the presented lossless compression for large number of test biometric images with software products, based on the new method, on the JPEG 2000 standard (lossless version) and on the FBI compression standard. Specific for the method for lossless compression is that it ensures very good results for processing of still images and for intra-frame compression of video sequences of graphic images, which is very important when the dynamic features of signatures are extracted. The new method for digital watermarking with IDP decomposition permits the insertion of different watermark in every pyramid level. Key words: Inverse Pyramid Decomposition, Biometric data processing, Lossless compression, Digital watermarking
1. Introduction The problems associated with people’s authentication are currently a significant part of numerous concerns in our modern society. Identity documents are tools that permit the bearers to prove or confirm their identity with a high degree of certainty. In response to the dangers posed by theft or fraudulent use of identity documents and national security threats, a wide range of biometric technologies is emerging, covering face, fingerprint, iris, or hand recognition. They are also proposed to facilitate border control and check-in procedures. These are positive developments and they offer specific solutions to enhance document integrity and ensure that the bearer designated on the document is truly the person holding it. Biometric identifiers – conceptually unique attributes - are today portrayed as the most reliable tool to verify someone’s identity [1,2]. In many countries, identity document is increasingly associated with biometry. All this does the efficient compression of biometric data very important. Another popular approach for people’s authentication is the visual comparison of an individual’s signature, which has been used for many
years as a method of identification and is now popular for electronic comparison in which signature dynamics such as pressure, speed, and direction of pen stroke are measured. The widely used methods for storage and processing of biometric information, based on the standards JPEG and JPEG2000 have one general disadvantage – they all need plenty of data to ensure reliable authentication and this is the main factor, which contributes to the problems related with the implementation of biometric information use: • the slow data transfer, resulting from the large amounts of data needed for the authentication; • the requirement to maintain large databases containing this data. In order to solve these problems the biometric data as a rule is compressed using different techniques, which in general decrease the image quality [3]. The new method for lossless image compression, presented here, ensures high compression ratio for the biometric data and as a result enables the faster transfer of the information needed for the authentication.
Another reliable tool for data protection is the digital watermarking. The efficient lossless compression presented below permits to use the biometric information as a digital watermark in the watermarking process or to hide the biometric information in the compressed image or multimedia data, increasing this way the reliability of the authentication systems. The paper is arranged as follows: in section 2 is presented the new Inverse Difference Pyramid (IDP) method for image decomposition and the algorithm for lossless data compression; in section 3 is presented the algorithm for digital watermarking, based on the IDP decomposition; section 4 presents the results obtained with the new method for lossless compression and the comparison with other widely used methods, and section 5 (Conclusion) presents the basic advantages of the new methods and the abilities for future applications in systems aimed at user authentication and secure data transfer.
2. Lossless image data compression The basic requirements for the image compression algorithms are the big compression ratio, the high quality of the restored image and the low computational complexity. As it is known [3], in practice are used some specially developed methods for lossless data compression. Most of them are based on the existence of a large number of same values in the processed data and their coding, as a rule is some kind of run-length or arithmetic coding. The inefficiency of the conventional approach is mainly a result of too many symbols being involved in the coding. Another disadvantage is that these methods are not adaptive to input statistics and known information. In order to achieve higher efficiency some authors developed modifications [4], which better suit the processed data.
2.1. Image decomposition two-level Inverse Difference Pyramid (IDP) The new method for still image compression, presented here, solves the problems with the efficient lossless compression of biometric data to a high degree. The essence of the method for image processing with Inverse Difference Pyramid (IDP) decomposition [5] will be explained below for 8-bit greyscale images. The IDP decomposition is performed as follows: the original image [B] with size m×n pixels is divided in K square sub-images with size 8x8 pixels. In case, that the processed image can not be divided in exact number of subimages, the number of pixels in the image is increased up to the necessary amount. The additional
pixels are arranged at the image sides (right and bottom for example) and they all have same brightness – grey level. When the original image is prepared for the processing in the described way, each sub-image is processed with two-dimensional (2D) orthogonal transform (in this case, WalshHadamard transform, WHT). The decomposition starts with the lowest IDP decomposition level, which as a rule consists of a small number of coefficients only. For the presented application the IDP decomposition starts with 4 coefficients, corresponding with the lowest spatial frequencies (truncated WHT transform): 7 7 for u = v = 0; ∑∑ B(i, j) i =70 j=70 B(i, j)(−1) j / 4 for u = 0, v = 1; ∑∑ i =0 j=0 (1) sˆ 0 (u,v) = 7 7 i / 4 for u = 1, v = 0; B ( i , j )( 1 ) − ∑∑ i=0 j=0 7 7 B(i, j)(−1) i / 4+ j / 4 for u = v = 1; ∑∑ i =0 j=0 0 − for u, v = 2,3,..,7,
where B(i,j) is a pixel of the sub-image and s0(u,v) is a transform coefficient. The calculated values of the transform coefficients comprise the lower “zero” level of the pyramid decomposition. Then the image is restored with inverse WHT, using the values of the participating 4 transform coefficients only, and is calculated the difference between the original and the restored (approximated) image. The obtained difference image is divided in sub-images with size 4x4 pixels and the same operations are performed again. The second (higher) pyramid level consists of the values of the full set of Walsh-Hadamard transform coefficients for all 4x4 sub-blocks (i.e. the participating coefficients are 16 for each sub-block), and this ensures the unchanged quality of the restored image. These operations are performed in accordance with the relations: ˆ (i, j)] = [ E1 ] [ E 2 ] [E (i, j)] = [B(i, j)] − [B [E ] [ E ] 4 3
(2)
ˆ (i, j)] = (1 / 64)[H(8)][sˆ (u , v)][H (8)] [B 0
(3)
[s1k (u,v)] = [H(4)][Ek (i, j)][H(4)] for k=1,2,3,4 (4) where: ˆ (i, j)] is the sub-image approximation; sˆ (u , v) [B 0 are the transform coefficients for pyramid level p=0; [H(N)] is a Walsh-Hadamard matrix of size N×N for N=8, 4; [E k (i, j)] are difference matrices of size 4×4, and s1(u,v) are the transform coefficients for pyramid level p=1.
The calculated IDP coefficients’ values are arranged in one-dimensional sequences in accordance with their spatial frequency. Each sequence contains the values of coefficients, corresponding with same spatial frequency from all sub-images in one IDP level. At the end of the arrangement all values from the two levels are arranged in one large sequence, {∆S}. After this operation follows special lossless data coding of this data sequence.
2.2. Histogram-Adaptive Run-Length Coding The data obtained in result of the IDP decomposition is processed with lossless Histogram-Adaptive RunLength (HARL) Coding. This part of the processing is based on one of the specific features of the image data processed with IDP and 2D Walsh-Hadamard transform: in result of the decomposition the values of most of the coefficients (in particular – in the higher pyramid level) are “zero”. The basic steps of the algorithm for HARL coding are as follows: • The processing starts with the calculation of the histogram h(∆S) of the coefficients values in the data sequence {∆S}; • The histogram is analyzed and are defined the parts, where h(∆S)=0. • The sequences of same values (most frequently equal to zero) in the data sequence are presented with some of the not-used histogram values in accordance with the following rules: - Sequences of zeros, with length smaller or equal than that of the longest sequence of not used values in the histogram, is replaced by a number, equal to the sum of the start value of the sequence of not used values and the length of the compressed sequence; - Sequences of zeros in the transformed data, longer than the sequence of not used values in the histogram, but shorter than 2mn, are represented with 2m words. The first of these words contains the start value of the longest sequence of not used values, the next (m-1) words contain zeros, and the remaining words – the length of the coded sequence; - Sequences of same values, different from zero, are represented in similar way, adding one more word, containing this value. - Very long sequences of same values (if there are any) are represented with additional word containing the sequence length. - At the end of the processing the code words are coded with modified Huffman code. The HARL data decoding is done performing the described operations in reverse order. The image restoration after IDP decomposition is performed in accordance with the relation:
ˆ (i,j)] + [E (i, j)] = [B ˆ (i,j)] + [E 1 ] [E 2 ] , (5) [B(i,j)] = [B [E ] [E ] 4 3
where: [E k (i, j)] = (1/ 16)[H (4)][s1k (u ,v)][H(4)] for k =1,2,3,4. The IDP decomposition of colour images is performed in similar way, building three IDP pyramids – one for each component, followed by lossless HARL coding. In general, the number of IDP decomposition levels could be 3,4,5, etc – depending on the processed image size and on the aimed application. Usually such multi-level decomposition is used for lossy image compression. The version with two pyramid levels is used only when lossless image compression is required.
3. Digital watermarking with IDP decomposition One of the ways to protect the contents of the digital information is to insert a watermark in it. As a rule, the watermark is a relatively small data sequence, which does not increase significantly the protected contents, retaining the image or sound quality perceptually unchanged [8]. In result of the efficient compression, the compressed biometric data (fingerprint, signature) could be used as a digital watermark, which will make the already mentioned authentication means even more reliable. The approach, presented below, offers new possibilities for fast and reliable user authentication in all kinds of data transfer and mobile communications. For example, it is possible in the dataflow of regular conversation to transfer the compressed fingerprint image, if the sending side (for example, a cell phone) is equipped with a special sensor and software. The hidden compressed fingerprint image (or signature or a voice sample) will be sent to the receiving side and extracted by the authorized staff for some application. The same approach could be used for IP protection in all kinds of multimedia data transfer. For these applications two basic approaches are possible: to insert a resistant or a fragile digital watermark in the protected information. As a rule, the resistant watermark is used to prove the contents ownership, and the fragile one – to point at an unauthorized contents change (editing). For this, the IDP decomposition is quite suitable because of its pyramid structure. The resistant watermark is embedded in the values of a part of the transform coefficients, selected in accordance with strictly set rules: these coefficients should be low-frequency ones, and their values should be above a pre-defined
threshold. The watermark is highly resistant against basic pirates’ attacks (JPEG compression, rescaling, cropping, histogram equalization, contrast enhancement, gamma correction), because the insertion is performed in the spectrum domain of the protected signal. Such watermarking is suitable for cases, when lossy compression is used. The fragile watermark is more suitable for lossless IDP compression. In this case, the watermark insertion is performed adding the watermark data to the compressed image information as an additional pyramid level (additional data). In case, that the watermark should be invisible, it is not visualized together with the protected image and its presence becomes obvious only on authorized request. The visualized watermark proves whether the image contents are authentic. The pyramid structure permits not only to insert some kind of a digital watermark in the processed data, but to use different watermark in every pyramid level as well. In result of the efficient lossless compression, the compressed biometric data can be used as a fragile watermark (for example, the signature image with size 300×180 pixels after compression is approximately 1,3 KBytes). Compared with a color image with size 800×600 pixels which is equivalent to 1 440 000 Bytes, the compressed fingerprint image data is negligible; the watermark data remains much smaller after IDP compression of the original color image with compression ratio 10 as well.
existing standard communication nets. The watermark insertion in the image data is equivalent to the creation of additional level in the pyramid. The decoding is done, performing the already described operations in reverse order: • The components Xp(r) and Wp(r) are extracted from the received data sequence Zp(r) and is restored the watermark image Wp(i,j) for the level р=0,1; X p (r ) = Z p (r ) for r = 1, 2, .., l p.
(7)
Wp (r ) = Z p ( r ) for r = l p + 1, l p +2, .., l p + l wp . (8) • The information Xp(r), obtained in result of the lossless compression, is decoded, and after that are calculated the values of coefficients s p (u r ,v r ) ; ~ • The approximation of the sub-image [B] /[ E ] is calculated, using inverse orthogonal transform, in accordance with (2) and (6); • The values of the elements B(i, j) of the restored image are calculated in accordance with (5). Similar approach could be applied for IDP decomposition with any number of levels. The block diagram of the watermarking method is presented in Fig.1. In this example is used two-level IDP decomposition. The method is explained for one sub-block of the input image, with size 8x8 pixels.
The image watermarking based on the IDP decomposition (5) permits the insertion of different watermark in every level of the IDP pyramid. For this, the elements Zp(r) for the level p of the data sequence, are represented as follows: for r = 1,2,.., l p ; X p (r ) Z p (r ) = Wp (r ) for r = l p + 1, l p +2, .., l p + l wp .
(6)
Here {Xp(r): r=1,2,..,lp} is the data sequence with length lp; Wp(r) is the compressed data with length lwp representing the watermark in the level р. The watermark image, which will be used as a watermark is compressed with lossless compression. For this is suitable to use relatively small watermark images (for example, 256×256 or 512×512 pixels) and in the process of decomposition to apply it on the watermarked image as many times, as necessary, to cover it. The following (higher) pyramid level creates a new data sequence Zp(r), where another watermark image could be inserted. Then all the information is arranged in one common data sequence. The data, obtained in result of the image compression, and the inserted watermark, is saved and stored, or sent to the receiver in accordance with the application, using the
Fig.1. Block diagram of the algorithm for watermark insertion with two-level IDP The decoding is presented in Fig.2. The IDP decomposition, presented with the block diagrams in Fig. 1and 2 could be extended for larger number of levels. The case, when the IDP decomposition consists of more than two levels is used for lossy image compression. In such case is possible to insert more than two watermarks. The method permits to use the two kinds of watermarks (resistant and fragile) together.
The comparison shows that the results obtained with IDP-HARL are better than these obtained with JPEG2000 (lossless version). The application of the new method for fingerprint compression in the up-todate databases will ensure significant decreasing of the required memory. In cases where the image should be transferred from one place to another a shorter time will be required to receive the information. As a result, the receiving side will be able to use the maximum data available for the user authentication and will obtain higher recognition reliability because the fingerprint image is sent with retained quality without slowing-down the system performance.
4. Experimental results 4.1. Lossless compression of fingerprints The presented method offers very good results when applied for compression of fingerprints, signatures, texts, and other graphic images. In order to present the method advantages it was compared with JPEG 2000 (lossless) version and with the FBI free evaluation (test) software. The comparison was performed with images of fingerprints and signatures. The testing was performed with more than 100 grayscale fingerprint images. In Fig. 3 are presented four of the used test images.
The comparison results, presented in Fig.4 show that the compression ratio obtained with the new method for lossless compression of graphic images is higher than the results obtained with JPEG2000 (lossless version) [5,6]: the mean value of the compression ratio, obtained with the new method for the presented image class (fingerprints) was 4,8; for JPEG2000 (LS) it was 2,2.
Compression ratio
Fig.2. Block diagram of the algorithm for watermark extraction with IDP
8 6
IDP
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JPEG2000
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Image No.
Fig. 4. Results for lossless compression of fingerprint images. Another comparison was done with the free FBI evaluation software, based on FBI standard [3, 7]. The results show that the compression ratio obtained with the IDP-HARL method is much higher and together with this the quality of the restored images for same compression ratio is better (PSNR is higher). This comparison was performed for lossy compression only, because there was not available lossless version of the FBI compression standard on Internet.
Fig. 3. Test fingerprint images (size 288×353 pixels, 8 bpp)
The results, presented in Fig.5.a (lossy compression) show that for same quality the compression ratio (CR) obtained using the IDPHARL method is much higher: the mean CR for the test FBI software is approximately 5, and for the new method it is higher than 8.
Compression Ratio
12 10 8 6 4 2 0 1
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Image No. IDP
FBI Test
PSNR [dB]
Fig. 5.a. Comparison of the compression ratio for visually lossless image quality 34 32 30 28 26 24 22 20
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used as a basis to develop a new tool for signature authentication, representing the signature as a sequence of TV frames, obtained in the process of signing. As a result, the size of a sequence of 40 consecutive TV frames is equal to the size of a single signature image only. In order to retain the image quality unchanged, the compression is intra-frame i.e. every TV frame is compressed independently. For this, the video file is treated as a sequence of still images. The obtained compression ratio becomes higher if the TV frames are arranged in groups of 8 – 12 consecutive frames and treated as one large image. The results, obtained from the processing of 1, 5, 6,.., 9 consecutive TV frames together for one of the tested signatures are presented in Table 1. The increasing of the obtained compression ratio is obvious. The presented results are for horizontal arrangement of the processed frames (i.e. they were arranged as one very wide image). Additional research was performed for vertical arrangement and arrangement in a rectangle, but the values obtained for the compression ratio, were close. The experiments showed that the processing of more than 8 consecutive TV frames together does not enhance the method efficiency.
12 14 21 23 25 32 34 Image No.
IDP
FBI Test
Fig. 5.b PSNR for same compression ratios Fig. 5. Comparison results for IDP and FBI standard (fingerprint images) In Fig. 5b are presented the values of PSNR for same compression ratios, obtained with the two software tools: for the test FBI software the mean value for the PSNR is 28, and for the IDP-HARL method based on the IDP decomposition it is above 31. The experimental results show that the performance of the new method for the tested fingerprint images is better both for the CR and for the restored image quality.
4.2. Lossless compression of signature images The compression of signature images is another application area for the presented method. The signatures used for the experiments were scanned and stored in bmp format. Then the background was filtered and smoothed, removing some of the noises natural for the scanned images. The line of the signatures was retained unchanged. In Fig. 6 are presented some of the test images. As a result of the high compression ratio obtained, the presented method offers another big advantage - it could be
Fig. 6.a. Size 300×170
Fig. 6.b. Size 300×160
Fig. 6.c. Size 300×170 Fig. 6. Test signature images This new approach allows not only to authorize the signature itself but also to analyze the movements of the hand in order to recognize whether the person is in stress or not (analyzing the tremor of the hand) and to use any other biometric and graphologists data, as pen stroke, speed, etc., which could be extracted from the video-clip information. Same approach is suitable for color signatures as well. Image L1h
L5h L6h L7h
L8h L9h Mean
HARLC 40,02 40,54 40,62 40,68 40,65 40,70 40,51 JPEG2 5,82 5.81 5.83 5.77 5.77
5.8
5,80
Table 1. Compression ratio obtained for sequence of TV frames treated as one image
The experimental results obtained with the image compression software for some of the test images are presented in Table 2. The results for the JPEG2000 compression were obtained with LuraTech Algovision, and these for IDP-HARL – with TKView, implementing the described new method. Compression Picture Ratio, IDP Sign1 36,23 Sign2 20,53 Sign3 25,24 Sign4 35,71
Compression Ratio JPEG2000 (LS) 6,82 9,90 5,22 6,85
Fig. 7.a,b. Example Watermark images
Table 2. Results of the lossless compression with TKView and JPEG2000. The results obtained with TKView, confirm the high efficiency of the method for compression of fingerprints, signatures, graphics, texts, etc. The specific advantages of the method are: • It ensures higher compression ratio for fingerprints and signature images than JPEG2000 (lossless version). • It has lower computational complexity than JPEG2000. This results from the fact that JPEG2000 is based on the wavelets transform, and the IDP decomposition – on the WHT. • It permits the still image compression to be applied in a new area - the lossless compression of a sequence of TV frames representing signatures, which to be used for dynamic biometric features extraction, necessary for some authentication procedures.
4. Experimental results for Digital watermarking with biometric data Usually the inserted watermark should be invisible (inaudible), but for certain applications the visible (audible) watermark is preferred. In cases, when such watermarks are used, they make the protected contents unusable, and the watermark removal is permitted for authorized personnel only. An example for visible watermark use is presented in Fig. 7. The original images were in bmp format and after processing with IDP decomposition they were compressed and watermarked in tk format. In the example the size of the watermark image is equal with that of the protected one (256×256 pixels), but usually the size of the inserted watermark is much smaller and it is inserted many times in the image contents so that to cover it. The compressed watermark size is negligible compared with the protected data: for example, the compressed data for the image in Fig.7.a (the squares) is 330 Bytes only. Similar compression ratio is obtained with IDP for the second example watermark image (Fig.7.b).
Fig. 7.c. Watermarked text image with embedded hiding watermark - the text could not be read The invisible fragile watermarks are used to prove unauthorized product use (editing, cropping, coping, etc.), but the method for watermark insertion could be used for data hiding as well. For example, the fingerprint image could be hidden (Fig. 8 a, b) in the regular data flow in the process of usual data transfer (pictures, music) or in the conversation (for mobile communications). This application is suitable (for example) for reliable authentication needed for bank transfers or other similar operations with high responsibility.
Fig. 8. a. Fingerprint image, used as watermark
The fingerprint image, which was used as watermark was compressed with lossless IDP compression and after that embedded in the compressed (tk format) image “Lena” as an additional IDP level. The watermark was visualized using a password.
The watermarking method developed as follows: - Development of methods for graphic watermark using the statistics; - Increasing the watermark compressions and editing, using transform instead of WHT.
will
be
further
the creation of a compressed data resistance against Complex Hadamar
Acknowledgement. This paper was supported by the Bulgarian Ministry of Education and Science (Contract № 1304).
References
Fig. 8.b. Example for watermarked test image with visualized hidden watermark (fingerprint)
5. Conclusion The obtained results show that the presented method ensures efficient lossless compression of biometric image data. The method is suitable for applications, which require efficient and reliable storage, archiving and transmission of some kinds of biometric information as fingerprints and signatures. The low computational complexity of the new method will permit its easy implementation for real-time tasks. The IDP-HARL method will be further developed in the following directions: - Development of an algorithm, permitting to set Regions of Interest (ROI) in the processed image. In result some sub-images will be processed with lossless compression, and the remaining ones – with a lossy, which will increase the method efficiency and will make the compression more flexible; - Development of an algorithm for adaptive filtering of signature images, retaining the meaning information; - Development of intelligent software able to present the signature process as a video clip, solving the problem with the efficient intra-frame coding; - Development of a version of IDP-HARL method for “nearly lossless” compression, similar with the FBI standard for fingerprint compression (the preliminary results for such compression show that the method efficiency is much higher).
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