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Fig. 1: Application example. User moves finger upon capacitive sensor, or slides optical sensor on an image or surface Ideal target applications are navigation/mouse and/or a portable scanner. We can also use the fingerprint sensor for non-critical security features; for example, to gain access to a consumer device, or simply to recall preferred personal settings. The user would move the sensor on a surface (see fig. 1) and by processing the input images, we are able to extract the movement occurred and/or to reconstruct the underlying global image.

Figure 1 Application example. User moves finger upon capacitive sensor, or slides optical sensor on an image or surface Ideal target applications are navigation/mouse and/or a portable scanner. We can also use the fingerprint sensor for non-critical security features; for example, to gain access to a consumer device, or simply to recall preferred personal settings. The user would move the sensor on a surface (see fig. 1) and by processing the input images, we are able to extract the movement occurred and/or to reconstruct the underlying global image.

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Fig. 2: Example of fingerprint scanning. By simply stacking the sensor output images (on the right), we have an unacceptable result
Fig. 2: Example of fingerprint scanning. By simply stacking the sensor output images (on the right), we have an unacceptable result
Fig. 3: Block-matching motion estimation technique The basic principle of block matching motion estimation (see fig. 3) is to use two images A and B. We select an area C inside first picture A that needs to be ‘motion estimated’ on the other and we look for an area D inside picture B that is as similar as possible to area C. The position of each area C, D is identified by the position of its upper-left pixel. The difference in position of these two pixels in the respective pictures is called motion vector (E), and it is the expression of the motion that area C underwent between the two images. The motion
Fig. 3: Block-matching motion estimation technique The basic principle of block matching motion estimation (see fig. 3) is to use two images A and B. We select an area C inside first picture A that needs to be ‘motion estimated’ on the other and we look for an area D inside picture B that is as similar as possible to area C. The position of each area C, D is identified by the position of its upper-left pixel. The difference in position of these two pixels in the respective pictures is called motion vector (E), and it is the expression of the motion that area C underwent between the two images. The motion
Fig. 4: Spatial/temporal recursive block-matching motion estimation In literature [7]-[13] there are other approaches that try to decrease the computational complexity by a focusing on the selection of candidate predictors. One among the most successful is the spatial-temporal approach [14]-[16]. It exploits the principle that when estimating a sequence of video images, results of successive estimations are not independent, but they instead tend to be strongly correlated. This is true spatially and temporally. Temporally means that if we perform
Fig. 4: Spatial/temporal recursive block-matching motion estimation In literature [7]-[13] there are other approaches that try to decrease the computational complexity by a focusing on the selection of candidate predictors. One among the most successful is the spatial-temporal approach [14]-[16]. It exploits the principle that when estimating a sequence of video images, results of successive estimations are not independent, but they instead tend to be strongly correlated. This is true spatially and temporally. Temporally means that if we perform
Algorithms following this approach exploit the correlation among motion vectors to decrease the number of matches and increase consistency of the results. It has to be noted that these algorithms have been developed for TV (one PAL image is 720x576 pixels) sequences digital compression. In this application motion estimation is performed on 16x16 pixels blocks, so there are a lot of vectors to be taken as spatial- temporal references (1620 for each frame). The algorithm will test a certain amount of such temporal and spatial vectors as the starting point of the estimation. A second ‘refine’ phase will test small variations of the first phase winner in order to see if a better match is available in some neighboring position (see fig. 4). Fig. 5: Recursive motion estimation applied to capacitive stripe (or optical) sensor output: we have only one vector per frame, but high frame rate
Algorithms following this approach exploit the correlation among motion vectors to decrease the number of matches and increase consistency of the results. It has to be noted that these algorithms have been developed for TV (one PAL image is 720x576 pixels) sequences digital compression. In this application motion estimation is performed on 16x16 pixels blocks, so there are a lot of vectors to be taken as spatial- temporal references (1620 for each frame). The algorithm will test a certain amount of such temporal and spatial vectors as the starting point of the estimation. A second ‘refine’ phase will test small variations of the first phase winner in order to see if a better match is available in some neighboring position (see fig. 4). Fig. 5: Recursive motion estimation applied to capacitive stripe (or optical) sensor output: we have only one vector per frame, but high frame rate
Fig. 6: Method for stitching sensor output images together to obtain the reconstructed picture
Fig. 6: Method for stitching sensor output images together to obtain the reconstructed picture
Fig. 7: Result of fingerprint reconstruction for 0.37 pixels/frame vertical motion, no horizontal motion. Starting image is particularly dark. White central lines show where picture would stop if reconstruction was perfect. Surrounding lines show +/- 10% stretching error marks
Fig. 7: Result of fingerprint reconstruction for 0.37 pixels/frame vertical motion, no horizontal motion. Starting image is particularly dark. White central lines show where picture would stop if reconstruction was perfect. Surrounding lines show +/- 10% stretching error marks
Fig. 8 Result of reconstruction for 0.6 pixels/frame vertical and 0.37 pixels/frame horizontal motion, starting from a smeared image. Grey area on the right is due to padding of the sequence generator, to be able to generate horizontally moving images. Results have demonstrated the validity of the algorithm, with both optical and capacitive sensors, for mouse and scanner application. Reconstructed fingerprints match in height and width very accurately the original ones, and trajectory of estimated motion follows very closely the original one. Fig. 7 through 49 show several fingerprints with different characteristics (bright, dark, noisy, smeared) scanned with a range of different speeds in horizontal and vertical directions.
Fig. 8 Result of reconstruction for 0.6 pixels/frame vertical and 0.37 pixels/frame horizontal motion, starting from a smeared image. Grey area on the right is due to padding of the sequence generator, to be able to generate horizontally moving images. Results have demonstrated the validity of the algorithm, with both optical and capacitive sensors, for mouse and scanner application. Reconstructed fingerprints match in height and width very accurately the original ones, and trajectory of estimated motion follows very closely the original one. Fig. 7 through 49 show several fingerprints with different characteristics (bright, dark, noisy, smeared) scanned with a range of different speeds in horizontal and vertical directions.
Fig. 9: Result of reconstruction for 0.89 pixels/frame vertical motion on a dark, noisy fingerprint image image already acquired to better center them. This is a topic for future research. Another obvious solution is to increase the sensor resolution to reduce the number of input images necessary to cover one full picture.
Fig. 9: Result of reconstruction for 0.89 pixels/frame vertical motion on a dark, noisy fingerprint image image already acquired to better center them. This is a topic for future research. Another obvious solution is to increase the sensor resolution to reduce the number of input images necessary to cover one full picture.
Fig. 10: Results of simulation of scanning a printed paper with the optical sensor and the combining the pictures with the proposed algorithm
Fig. 10: Results of simulation of scanning a printed paper with the optical sensor and the combining the pictures with the proposed algorithm
VI. IMPLEMENTATION COMPLEXITY
VI. IMPLEMENTATION COMPLEXITY
Fig. 12: Simulation results for optical sensor: background image is a piece of printed tissue Figures 11 through 14 show the simulations results of optical sensor moving on various surfaces. For each case, the surface tested is shown as background of the picture. Two tracks are plotted, a light-gray one is the input movement that we want to estimate, and the dark-gray one is the results of the estimations. If estimation is perfect, this second line is completely superimposed to the first one. As pictures show, tracking is good on dark, bright, detail-rich, repeated patterns, and uniform images. Original tracks are logged movements of a ball mouse, fed to the sequence generator presented above. In particular, estimations of movements are correct within 99.9% overall. Table I shows percentages of correct estimations for the surfaces tested
Fig. 12: Simulation results for optical sensor: background image is a piece of printed tissue Figures 11 through 14 show the simulations results of optical sensor moving on various surfaces. For each case, the surface tested is shown as background of the picture. Two tracks are plotted, a light-gray one is the input movement that we want to estimate, and the dark-gray one is the results of the estimations. If estimation is perfect, this second line is completely superimposed to the first one. As pictures show, tracking is good on dark, bright, detail-rich, repeated patterns, and uniform images. Original tracks are logged movements of a ball mouse, fed to the sequence generator presented above. In particular, estimations of movements are correct within 99.9% overall. Table I shows percentages of correct estimations for the surfaces tested
Fig. 11: Simulation results for optical sensor. The black line is estimated trajectory; the white one is the reference trajectory. The white one is thicker for readability. The two lines are very well superimposed, meaning estimation produces correct results. In the background the test image feed to the sequence generator. In this case, a printed paper
Fig. 11: Simulation results for optical sensor. The black line is estimated trajectory; the white one is the reference trajectory. The white one is thicker for readability. The two lines are very well superimposed, meaning estimation produces correct results. In the background the test image feed to the sequence generator. In this case, a printed paper
Fig. 13: Simulation results for optical sensor: background image is a mouse pad back
Fig. 13: Simulation results for optical sensor: background image is a mouse pad back
Fig. 14: Simulation results for optical sensor: background image is a black desk We have shown that the algorithm is working very well in terms of image reconstruction and mouse pointer, for both sensor inputs. We will now describe the complexity involved
Fig. 14: Simulation results for optical sensor: background image is a black desk We have shown that the algorithm is working very well in terms of image reconstruction and mouse pointer, for both sensor inputs. We will now describe the complexity involved
Fig. 15: Proposed architecture of the integrated mouse and scanner. To complete the implementation, we also need two frame buffers, less than 1kB total, and some very simple control
Fig. 15: Proposed architecture of the integrated mouse and scanner. To complete the implementation, we also need two frame buffers, less than 1kB total, and some very simple control
Fig. 16: real-time optical mouse prototype Results in both the applications were optimal; we were able to use the optical sensor as ‘the’ mouse of the PC where the algorithm was running, without any problem of incorrect tracking or drift. We were also able, with the capacitive sensor setup, to capture finger images, feed them to finger recognition software and get correct identifications output. Correct means both getting identification when comparing two fingerprints of the same finger, and denying identification when comparing two fingerprints of different fingers.
Fig. 16: real-time optical mouse prototype Results in both the applications were optimal; we were able to use the optical sensor as ‘the’ mouse of the PC where the algorithm was running, without any problem of incorrect tracking or drift. We were also able, with the capacitive sensor setup, to capture finger images, feed them to finger recognition software and get correct identifications output. Correct means both getting identification when comparing two fingerprints of the same finger, and denying identification when comparing two fingerprints of different fingers.
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