Fingerprint image compression by a natural clustering neural network

Chang, W.; Soliman, Hamdy; Sung, Andrew H.

doi:10.1109/icip.1994.413588

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…The existence of these strong structures has led researchers to use Sparse Representation (SR). For instance [34,35] uses Self Organizing Map (SOM) -is kind of SR -to represent patches of fingerprint images in a compressed format. [34] used SOM and Learning Vector Quantization (LVQ) to learn the network, while [35] used Wave Atom Decomposition and SOM.…”

Section: Previous Workmentioning

confidence: 99%

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Parallel Stroked Multi Line: a model-based method for compressing large fingerprint databases

Mansouri¹,

Pourreza²

2016

Preprint

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With increasing usage of fingerprints as an important biometric data, the need to compress the large fingerprint databases has become essential. The most recommended compression algorithm, even by standards, is JPEG2000. But at high compression rates, this algorithm is ineffective. In this paper, a model is proposed which is based on parallel lines with same orientations, arbitrary widths and same gray level values located on rectangle with constant gray level value as background. We refer to this algorithm as Parallel Stroked Multi Line (PSML). By using Adaptive Geometrical Wavelet and employing PSML, a compression algorithm is developed. This compression algorithm can preserve fingerprint structure and minutiae. The exact algorithm of computing the PSML model take exponential time. However, we have proposed an alternative approximation algorithm, which reduces the time complexity to O(n 3 ). The proposed PSML algorithm has significant advantage over Wedgelets Transform in PSNR value and visual quality in compressed images. The proposed method, despite the lower PSNR values than JPEG2000 algorithm in common range of compression rates, in all compression rates have nearly equal or greater advantage over JPEG2000 when used by Automatic Fingerprint Identification Systems (AFIS). At high compression rates, according to PSNR values, mean EER rate and visual quality, the encoded images with JPEG2000 can not be identified from each other after compression. But, images encoded by the PSML algorithm retained the sufficient information to maintain fingerprint identification performances similar to the ones obtained by raw images without compression. One the U.are.U 400 database, the mean EER rate for uncompressed images is 4.54%, while at 267:1 compression ratio, this value becomes 49.41% and 6.22% for JPEG2000 and PSML, respectively. This result shows a significant improvement over the standard JPEG2000 algorithm.

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Section: Previous Workmentioning

confidence: 99%

“…For instance [34,35] uses Self Organizing Map (SOM) -is kind of SR -to represent patches of fingerprint images in a compressed format. [34] used SOM and Learning Vector Quantization (LVQ) to learn the network, while [35] used Wave Atom Decomposition and SOM. Also, k-SVD [10] is used for sparse representation of image patches.…”

Section: Previous Workmentioning

confidence: 99%

Parallel Stroked Multi Line: a model-based method for compressing large fingerprint databases

Mansouri¹,

Pourreza²

2016

Preprint

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“…The earlier work in fingerprint compression includes neural network [2], which has high utilization of neurons, robust clustering, and feasible for VLSI implementation resulting into high training time. Lattice Vector Quantization method [3], another work in this domain, yields a high compression ratio with a moderate computational load.…”

Section: Introductionmentioning

confidence: 99%

ANovel prediction algorithm for lossless compression of fingerprint images

Singla

Anand

Tiwari

et al. 2012

2012 IEEE 11th International Conference on Signal Processing

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In this paper we propose a new prediction scheme for lossless compression of fingerprint images. The proposed method uses the symmetric/near symmetric property of such images. The scheme first divides the image horizontally into two halves and then the upper half is divided vertically in another two halves; resulting in three parts of the given image. It is observed that the two halves of the upper part are highly correlated. In view of this observation, the pixels of the right half are predicted using those in the left half and also in the present half. On the other hand, pixels of the left half and those of the bottom half are classified as per slope bins of GAP and then LS based predictor is used with respective classes. Prediction performance of the proposed scheme is found to be significantly better than many competitive and popularly used methods.

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Introduction to Biometrics

Zhang

2000

Automated Biometrics

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Fingerprint image compression by a natural clustering neural network

Cited by 5 publications

References 23 publications

Parallel Stroked Multi Line: a model-based method for compressing large fingerprint databases

Parallel Stroked Multi Line: a model-based method for compressing large fingerprint databases

ANovel prediction algorithm for lossless compression of fingerprint images

Introduction to Biometrics

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