Experimental comparison of binary joint transform correlators used for fingerprint identification

Grycewicz, Thomas J.; Javidi, Bahram

doi:10.1117/1.600856

Cited by 19 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The target fingerprint image is loaded on an spatial hght modrdator, (SLM) Although the spatial heterodyning technique, often called joint transform correlator [8], has many advantages for real-time apphcations [9,10] and was used ia most of the recent fingerprint recognition experiments [11, 12, The exact mechanism for the significant increase in SNR for binary inputs is not completely understood. However, several previous works [17,18] 14. In previous optical fingerprint correlation studies [11,12,13,14,15,16], decreasing image size and sampling frequency has decreased sensitivity to rotational alignment and plastic distortion.…”

Section: Global Optical Correlationsmentioning

confidence: 99%

“…However, several previous works [17,18] 14. In previous optical fingerprint correlation studies [11,12,13,14,15,16], decreasing image size and sampling frequency has decreased sensitivity to rotational alignment and plastic distortion. of the fingerprint would be sampled with a resolution less than the expected Nyquist limit.…”

Section: Global Optical Correlationsmentioning

confidence: 99%

See 1 more Smart Citation

Effect of resolution and image quality on combined optical and neural network fingerprint matching

Wilson

Watson

Paek

1998

View full text Add to dashboard Cite

Section: Global Optical Correlationsmentioning

confidence: 99%

Section: Global Optical Correlationsmentioning

confidence: 99%

Effect of resolution and image quality on combined optical and neural network fingerprint matching

Wilson

Watson

Paek

1998

View full text Add to dashboard Cite

“…Although the spatial heterodyning technique, often called joint transform correlator [8], has many advantages for real-time applications [9,10] and was used in most of recent fingerprint recognition experiments [11,12,13,14,15,16], the VanderLugt correlator was adopted Figure 1 : Diagram of the optical pattern recognition system in this experiment. This is because the VanderLugt correlator does not require a fast SLM with high resolution and the large SBP (space bandwidth product) available from holographic recording materials provide high degree of freedom to accommodate various distorted versions of a target that are simultaneously compared with an input.…”

Section: Global Optical Correlationsmentioning

confidence: 99%

<title>Combined optical and neural network fingerprint matching</title>

Wilson

Watson

Paek

1997

Optical Pattern Recognition VIII

View full text Add to dashboard Cite

This paper presents results on direct optical matching of inked and real-time fingerprint images. Direct optical correlations and hybrid optical neural network correlation are used in the matching system for inked fingerprints. Preliminary results on optical matching of real-time fingerprints use optical correlation. The test samples used in the inked image experiments are the fingerprint taken from NIST database SD-9. These images, in both binary and gray level forms, are stored in a VanderLugt correlator [1]. Tests of typical cross correlations and auto correlation sensitivity for both binary and 8 bit gray images are presented. When global correlations are tested on a second inked image results are found to be strongly influenced by plastic distortion of the finger. When the correlations are used to generate features that are localized to parts of each fingerprint and combined using a neural network classification network and separate class-by-class matching networks, 84.3% matching accuracy is obtained on a test set of 100,000 image pairs. Initial results with real-time images suggest that the difficulties resulting from finger deformation can be avoided by combining many different distorted images when the hologram is constructed in the correlator. Testing this process will require analysis of 10-20 second sequences of digital video.

show abstract

“…Optimized filters [6], [7], [8], [9], [10], the incorporation of three-dimensional information [11], [12], multiple input images [13], [14], [15], pre-and post-processing [16], [17], [18], and different nonlinear techniques [19], [20], [21], [22] are suitable methods to improve the correlator performance. In this article, we concentrate on an iterative correlation technique for the detection of small objects in cluttered scenes (non-overlapping noise).…”

Section: Introductionmentioning

confidence: 99%

Iterative nonlinear joint transform correlation for the detection of objects in cluttered scenes

Haist

Tiziani

1999

Optics Communications

View full text Add to dashboard Cite

Abstract-An iterative correlation technique with digital image processing in the feedback loop for the detection of small objects in cluttered scenes is proposed. A scanning aperture is combined with the method in order to improve the immunity against noise and clutter. Multiple reference objects or different views of one object are processed in parallel. We demonstrate the method by detecting a noisy and distorted face in a crowd with a nonlinear joint transform correlator.

show abstract

Experimental comparison of binary joint transform correlators used for fingerprint identification

Cited by 19 publications

References 0 publications

Effect of resolution and image quality on combined optical and neural network fingerprint matching

Effect of resolution and image quality on combined optical and neural network fingerprint matching

<title>Combined optical and neural network fingerprint matching</title>

Iterative nonlinear joint transform correlation for the detection of objects in cluttered scenes

Contact Info

Product

Resources

About