Quality Prediction in Fingerprint Compression

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303

Quality Prediction in Fingerprint Compression T. Pavithra1 1

2

Kalasalingam Institute of Technology, ECE, anu20bharathy@gmail.com

P. Anu bharathy2 Kalasalingam Institute of Technology, ECE anu20bharathy@gmail.com

Abstract— A new algorithm for fingerprint compression based on sparse representation is introduced. At first, dictionary is constructed by sparse combination of set of fingerprint patches. Designing dictionaries can be done by either selecting one from a prespecified set or adapting a dictionary to a set of training signals. In this paper, we use K-SVD algorithm to construct dictionary. After computation of dictionary, the image gets quantized, filtered and encoded. The resultant image obtained may be of three qualities: Good, Bad and Ugly (GBU problem). In this paper, we overcome the GBU problem by prediction the quality of image.

Index Terms— Compression, DCT, DWT, Fingerprint, Histogram, K-SVD, Sparse representation.

——————————  ——————————

1 INTRODUCTION

D

ue to the uniqueness of fingerprint, it is considered to be the most important of all biometric characteristics. Fingerprint has been widely used in identification of persons. Due to the advancement in technology person identification becomes digitalized. The fingerprint is applied in crime branches like FBI, forensic, etc. The fingerprint recognition becomes popular due to its simplicity. It is comprised of mainly ridges and valleys. The older technique used in fingerprint compression is based on wavelet scalar quantization [1]. K-SVD algorithm (K means clustering) is an iterative method that uses sparse coding for the current dictionary and continuously updating the dictionary. The K-SVD algorithm is compatible with many existing pursuit method [2]. Here, the training sample is considered by having both the corrupted image and the high quality image databases. The proposed system has the ability to predict whether the resultant image is Good (easy to match), Bad (average matching difficulty) and the Ugly (difficult to match) [3].

3

SPARSE REPRESENTATION

Representing an image in sparse is nothing but representing them in a few points. This greatly reduces the memory required to store them. In order to overcome the shortcomings like deformation, rotation, translation, noise, sparse representation should be employed [8]. The concept of sparse representation [9] is briefly explained below.

Sparse representation is nothing but considering only the value of few coefficients into account and others into zero. Equation (1) represents the sparse representation of vectors as follows,

(1) Only some of the co-efficient are considered in Fig.1.As a result, the data vector can be represented using few points.

2 EXISTING TECHNIQUE For general image compression, two most commonly used transforms are i) Discrete Cosine Transform [5], ii) Discrete Wavelet Transform [6]. DCT based algorithms are used in JPEG [7], JPEG2000 [8] whereas DWT based algorithms are used in SPIHT (Set Partitioning in Hierarchical Trees. Targeted at fingerprint images commonly used are WSQ (Wavelet Scalar Quantization), CT (Contourlet Transform) [1]. But these algorithms have a major disadvantage i.e.) they lack the ability of learning. The proposed method based on sparse representation has the ability to update itself.

————————————————

 T. Pavithra is currently pursuing bachelors degree program in electronic and communication engineering in Kalasalingam Institute of Technology, India, PH- +91 9994711434. E-mail: tpavithra333@gmail.com  P. Anu bharathy is currently pursuing bachelors degree program in electronic and communication engineering in Kalasalingam Institute of Technology, India, PH- +91 9489009651. E-mail: anu20bharathy@gmail.com

Fig.1.Sparse representation

111

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303 4

STEPS INVOLVED IN FINGERPRINT COMPRESSION

4.1 Dictionary Construction The K-SVD algorithm is used for dictionary construction.

Initially, the training set is constructed form the fingerprint samples.

A fingerprint sample is taken and is divided into fixed square patches using greedy algorithm and it is added to the training set [2].

At first, a new patch is added to the empty dictionary. Then, the next patch is taken and it is compared with the previous patches, if it is similar then it is left behind. If not, then it is added to the dictionary. The optimization problem is solved by using equation (2) in order to measure the similarity between the two patches.

Fig.2.Algorithm for fingerprint compression

(2) Here, ||● ||

2 F

is the Frobenius norm. The corresponding

matrices of two patches are P 1 and P 2 . t is a scaling factor which is the parameter of optimization problem.

4.2 Methods to construct the dictionary  Random method: The fingerprint samples from the training samples are selected at random and arranged as columns of the dictionary matrix.  Orientation method: Based on orientation, interval [0°, … ,180°] are divided into equal size intervals. An orientation (mid-value of interval) is assigned to each interval. Foreground patches of a fingerprint have an orientation while the background patches don’t. So, the patches with same orientation are taken and are arranged into a dictionary. For each interval, the same number of patches is taken.  K-SVD method: By continuously solving the optimization problem the dictionary is obtained [10]. It is given in equation (3), (3) Here, A is dictionary, Y consists of training patches, X are the coefficients, Xi is the ith column of X. The coefficient matrix X is solved by MP (Matching Pursuit) method. SVD (Singular Value Decomposition) is used to update the dictionary [2].

4.4 Fingerprint compression A new fingerprint is taken and it is divided into square patches which are of same size as that of test patches. The size of the patch is directly proportional to the compression efficiency. The size must be larger in order to achieve high compression efficiency. But this also increases the size of the dictionary. So some special care must be given in choosing the size of the patch.

For every patch, mean value, coefficient, location, the number of atoms to use are to be recorded. The mean value is recorded and subtracted from the patch in order to make the patches fit the dictionary better. Next, the sparse representation is computed by solving the l0 problem. Here, the coefficients whose values are less than given threshold are considered as zero. By this, only few coefficients are required to represent many image patches. So, it is better than use of fixed number of coefficients.

4.5 Encoding and quantization The atom number and mean value of each patch is separately coded. Coding of atom number, mean value, coefficient, their location is carried out by static arithmetic coders [12].

Lloyd algorithm [11] is used for quantization of coefficients. In each block, the first coefficient should be quantized with larger number of bits compared with other coefficients.

The image is also passed through the histogram equalizer, low pass filter, downsampler. Hence the noise is removed from the resultant image.

5

4.3 Training set construction The third method, K-SVD is the best method to construct the dictionary. For a good dictionary, the number of training samples should be high. This is determined based on the value of PSNR. Higher the PSNR, larger will be the number of training samples [10].

To construct test samples using fingerprint, minutiae (minute details), ridge frequency, and orientation are to be considered. In order to achieve good performance, the size of the dictionary is about 27000. However, this algorithm has the ability of learning. Hence we can add any number of samples in future [2].

Fig.2 is the diagrammatic representation of the steps involved in fingerprint compression.

PROPOSED METHOD

Usually, fingerprints are obtained by rolling an inked finger on paper and then scanning it using scanner. In advanced methods, the fingerprints are scanned with the help of projector and camera. In either way, all the fingerprints cannot be of same quality.

Applications such as password using fingerprint requires good quality images while for survey process, the bad quality image is enough. It is necessary to identify the quality of the fingerprint images so that the low quality images can be enhanced. The output efficiency is larger.

There is a necessity for computing sharpness metric, they are sensitive to blur. The blurred image shows dropping of the metric value. Singular value decomposition is used for the computation of metric value [4].

112

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303 The difference between the matching pairs is computed with the help of calculating hue and saturation levels in the image [3].

The good quality image is represented in Fig.3.

The original image taken for compression is shown in Fig.6.

Fig.3. Good quality image

The bad quality image is represented in Fig.4.

Fig.6.Original image

The data distributed can be graphically represented using histogram. Histogram equalization is nothing but a process of contrast adjustment using histogram of an image. The resultant image produced after histogram equalization is shown in Fig.7.

Fig.4. Bad quality image

The ugly image is represented in Fig.5,

Fig.7. Histogram equalization

Fig.5. Ugly image

6

EXPERIMENTAL RESULTS

The fingerprint compression can be achieved through many steps including histogram equalization, ridge segmentation, low pass filter, sampling, arrangement based on orientation field.

Fingerprints are comprised of ridges and valleys. Segmentation of fingerprint into smaller regions is necessary as it helps us to identify the local image features such as ridges and valleys. The segmentation is done based on ridges is shown below in Fig.8.

113

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303

Fig.8.Ridge segmentation

Fig.10.Low pass filtered image

For fingerprints, recognition based on orientation field is necessary.

Because this represents the necessary fields like core, orientation angle in an image.Fig.9 shows the orientation of the original image.

Sampling is done to convert analog to discrete values.

Downsampling is a method used to reduce the bit rate so that it can be transmitted over smaller bandwidth. The downsampled image is shown in Fig.11.

Fig.11.Downsampled image

Fig.9.The orientation field

In order to remove noise from the image, a low pass filter is used.

This filter in turn retains only the low frequency information by reducing the high frequency information. The low pass filtered image is shown in Fig.10.

114

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303

7

CONCLUSION

A new fingerprint algorithm is introduced. This algorithm works best especially at high compression rates when compared to other existing algorithms like JPEG, JPEG 2000, WSQ, etc. Also this algorithm retains more minutiae details of an image even after reconstruction. But, our algorithm has more complexities because of block by block processing method. But it is nothing when compared with complexities in JPEG method. Since the quality of fingerprint image is found previously, its efficiency can be greatly increased. If the image is of poor quality, then it can be increased by some suitable enhancement techniques.

REFERENCES [1] Guangqi shaos, Yanping wu, Yong A, Xiao Liu, and Tiande Guo, ”Fingerprint compression based on sparse representation”, IEEE Trans., vol.23, no.2, pp.489-501,Feb 2014.

[2] M. Aharon, M.Elad and A.M. Bruckstein,” The K-SVD: An algorithm for designing for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal process, vol.54, pp.4311-4322, 2006.

[3] Gaurav Aggarwal, Soma Biswas, Patrick J. Flynn and Kevin W. Bowyer, “Predicting Good, Bad and Ugly match pairs”.

[4] JitendraChoudary, Dr.Sanjeev Sharma, Jitendra singh Verma,” A New framework for improving low quality fingerprint images” Int. J. Comp. Tech. Appl., Vol 2 (6),1859-1866.

[5] N. Ahamed, T. Natarajan, and K. R. Rao, “Discrete cosine transform,” IEEE Trans. Comput., vol. C-23, no. 1, pp.90-93, Jan. 1974.

[6] C.S.Burrus, R.A.Gopinath, and H.Guo, “Introduction to Wavelet and Wavelet Transforms”: A Primer .Upper Saddle River, NJ,USA: Prentice-Hall,1998.

[7] W.Pennebaker and J. Mitchell, JPEG- Still Image Compression Standard.New York, NY, USA: Van Nostrand Reinhold, 1993.

[8] M.W. Marcellin, M. J. Gormish, A. Bilgin, and M.P.Boliek, “An Overview of JPEG-2000,” in Proc. IEEE Data Compress. Conf., Mar 200, pp. 523-541.

[9] Ke Huang and Selin Aviyente, “Sparse Representation for Signal Classification”, Michigan State University, MI 48824.

[10] Cristian Rusu and Bogdan Dumitrescu, “Stagewise K-SVD to design efficient dictionaries for sparse representation” July 182012.

[11] S.Lloyd, “Least squares quantization in PCM” IEEE Trans.Inf.Theory, vol.28, no. 2, pp.129-137, Mar. 1982.

[12] K.Sayood, Introduction to Data Compression, 3rd ed.San Mateo, CA, USA: Morgan Kaufman, 2005, pp. 81-115.

115