CHAPTER 2 LITERATURE REVIEW
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1 9 CHAPTER 2 LITERATURE REVIEW 2.1 INTRODUCTION In this chapter the literature available within the purview of the objectives of the present study is reviewed and the need for the proposed work is discussed. 2.2 REVIEW OF FINGERPRINT IMAGE CLASSIFICATION, VERIFICATION AND COMPRESSION ALGORITHMS Benhammadi et al (2008) have made an attempt in introducing three fast fingerprint matching algorithms namely, neigbourhood minutiae binary code matching, neighbourhood minutiae orientation code matching and global score matching algorithm. The proposed algorithms have been designed in such a way that they match the fingerprints with neighbouring details of minutiae along with the core point. They have tested the performance and proved the feasibility of the algorithm in terms of False Rejection Rate and False Acceptance Rate (Campbell et al 1996) by comparing the fingerprints available with two different databases namely DB1-a and DB2-a of FVC Cha and Kim (2008) have proposed a novel approach using fingerprint based biometrics to generate one time password for user authentication in internet security. They have generated random prime
2 10 numbers using the unique features of fingerprints to generate a unique one time password. They have validated their proposed model with their simulation results and substantiated their advantages over other methods. Kumar et al (2008) have made a detailed survey on the application of fingerprint based biometrics for cash payment. Kumar et al (2008) have discussed the various disadvantages of using credit cards, check cards, subway cards and so on and risk involved in remembering the passwords. Moreover they have discussed the significant advantages of using e-passwords generated using biometrics and also noted many aspects related to the fingerprint template and its classification. Based on their results, they have concluded that card payment system should be replaced with biometric based payment system to have easier, reliable, secure, cash free and tension free payment system. Anil et al (2008) have discussed some of the major difficulties encountered with the latent fingerprints when compared to that of rolled and plain. In addition they have made an attempt on the implementation of minutiae based matching algorithms namely, local minutiae matching, global minutiae matching and matching score computation for latent fingerprint identification. To speed up the matching process, they have also included the north-most core point in addition to the details of minutiae. They have experimented and proved the performance of their proposed algorithm (MS algorithm) with 258 latent fingerprints available in NIST SD27 database with their corresponding rolled and plain fingerprints. Gu et al (2006) have made an attempt in combining the global structure as well as local cues i.e., details of minutiae to propose a novel representation for fingerprint verification. They have pointed out certain limitations with the existing algorithms that they suffer difficulty in clubbing
3 11 the minutiae based details with the orientation field in the feature template. Gu et al (2006) have tested the performance of their proposed model with the fingerprints available in two databases with 7496 samples. They have compared the results obtained using their proposed model with those of ones obtained by Anil et al (2000), Ross et al (2003) and Anil and Hong (1997) and found that the error rate is lower than that of the others. Hiew et al (2006) have reviewed the existing algorithms used for the fingerprint image pre-processing. They have employed the use of digital camera for capturing the fingerprints and the same have been pre-processed for further processing during feature extraction. Besides they have discussed the importance of reliable touch-less fingerprint recognition. In their work they have discussed the significance of core point detection and presented how the digital camera can be effectively used for core point detection in fingerprints. Hiew et al (2006) have conducted 1938 experiments with the fingerprints taken by Canon PowerShot Pro 1 digital camera. Results obtained using their proposed algorithm have been compared with those of one obtained using Hong enhancement, Pseudo match filtering and STFT analysis. The same results have been further adopted for core point detection. Finally they have demonstrated the feasibility of their approach in terms of accurate core point detection over the other approaches. Guo (2005) has made an attempt on fingerprint matching using Hidden Markov Model (HMM). He has developed a novel model using HMM to extract the features of fingerprints like orientation angle and texture of neighbouring region. He has tested the performance of his proposed model with 880 fingerprint images taken from FVC2002-DB1 database and substantiated the performance of his model with satisfactory confidence coefficient, FRR and FAR. In addition he has claimed that the proposed
4 12 algorithm need not require any thinning process to extract the features of fingerprints. Sudhakar et al (2005) have made an attempt on the application of a modified form of wavelet based contourlet transform for fingerprint compression and discussed the various issues related to two-dimensional tensor product wavelet. They have compared the results in terms of compression ratio for the original image and that of the one obtained by their fingerprint compression techniques. Sudhakar et al (2005) have finally tested the samples to study the performance of their algorithm and claimed the advantages of their proposed model with their results. Shah and Sastry (2004) have presented a novel line detector algorithm for fingerprint identification, feature extraction and classification. Based on Henry classification method, Zhang et al (2002) and Shah and Sastry (2004) have classified the fingerprints in a similar fashion. They have presented three types of classifiers namely support vector machines, nearest neighbour classifiers and neural network classifiers and their performances were tested with the fingerprints available in NIST database. Finally they have claimed the precise performance of their proposed models with their experimental results and concluded that their model performed well in extracting crucial information needed for fingerprint classification. Gerek and Cetin (2000) have made an attempt on the implementation of adaptive polyphase subband decomposition structures for image coding. They have pointed out a few major disadvantages with the existing compression algorithms, especially in filter banks that they can not manage any sudden changes in input signal and out of which only poor quality images are produced. In order to overcome this limitation, they have introduced adaptive filter banks for image compression such that they
5 13 generate better quality outputs with high compression ratio. As well they have discussed many issues related to adaptive filtering, subband decomposition, efficient complex or real valued filter design methods. Gerek and Cetin (2000) have implemented their proposed model for images having combination of texts and images. Finally, they have claimed the worthiness of their adaptive filter banks over fixed filter banks Park and Ko (2003) have made an attempt on on-line fingerprint verification. They have adopted a novel reference point detection method for detecting, feature extraction and matching. Their proposed model exploits the properties of Gradient Probabilistic Model (GPM) that captures the curvature information of fingerprint texture. They have brought out the major limitations with the existing filter bank algorithms. They have claimed the better performance of their proposed model by comparing their results with those obtained using Poincare algorithm. They have concluded that their proposed model performs in a low noise environment better than the conventional approaches. Zhang et al (2002) have made an attempt in increasing the accuracy of fingerprint classification with the help of a novel method, namely pseudoridge tracing. They have discussed the five major categories of Henry fingerprint classification such as arch, tented arch, left loop, right loop and whorl. Moreover they have pointed out some of the major difficulties encountered in images such as noise that impede the process of feature extraction. With the use of their proposed pseudoridge tracing method, they have tested nearly 4000 fingerprint images. With the help of their test results they have claimed many advantages of their proposed model over other methods.
6 14 Bazen et al (2000) have made an attempt on the application of Correlation-Based algorithm for fingerprint verification. In correlation based algorithm, Bazen et al (2000) have introduced a template form of comparison of fingerprint verification instead of matching them with the details of minutiae. The correlation-based approach has been designed in such a way that it uses gray-scale information for generating the primary templates. They have stated that their proposed model is capable of dealing even with bad quality fingerprints to yield correct feature information. They have substantiated the viability of their proposed model by testing 880 fingerprints and obtained satisfactory results as that of other similar algorithms. Finally they have noted that the proposed model consumes a lot of computation time i.e., CPU time for feature extraction. Anil et al (1999) have discussed the importance of fingerprint classification and introduced a novel representation i.e. Fingercode based on a two-stage classifier for fingerprint classification. They have tested nearly 4000 fingerprint samples available in NIST-4 database with a Two Stage classifiers that employs the K-Nearest Neighbour classifier at its first stage and Neural Network classifier at its second stage. They have substantiated the dominance of their proposed multi-channel filter based classification algorithm based on the accuracy of fingerprint classification with the previous results reported in the literature in NIST database. Perona (1998) has discussed some of the major issues related to diffusions in image processing. He has proposed an algorithm for diffusion of orientation like quantities using gradient descent of an energy function inspired by the model of simple physical systems. Based on his numerical experiments, he has concluded that the proposed method de-noises the data related to orientation like quantities and more conclusions are also drawn with
7 15 respect to singularities in continuous and discrete space but are not substantiated. Maio and Maltoni (1997) have made an attempt on the implementation of gray-scale based minutiae detection method for feature extraction in fingerprints. They have attempted to extract the details of minutiae directly from gray-scale images instead of extracting them from the thinned fingerprint images. They have demonstrated the performance of the gray-scale detection approach by testing 150 different real samples of fingerprints. From their test results they have concluded that their approach yields low percentage of error in terms of false minutiae and claimed that the computational time is much lower than that of the other approaches. Davies and Plummer (1981) have made a detailed study on the application of thinning algorithms in the field of image processing. They have pointed out some of the issues prevailing in existing algorithms and brought out three major problems in the process of thinning namely, maintaining connectedness, retaining end-points and symmetric stripping. Considering all these above said issues, they have designed a new algorithm for thinning that includes propagate, mark, slim, thin, clean and purge. Finally they have concluded with the viability of their proposed model with their experimental results. 2.3 NEED FOR THE PRESENT WORK A critical review of the literature shows that majority of the work in the field of security systems, forensic and law enforcement have been facilitated with biometrics using fingerprints, retina patterns, face, ear, iris etc., for login enrolment and authentication (Engel et al 2008, Ge et al 2008, Shin et al 2008, Swaminathan et al 2008, Kwon and Moon 2008, Akhloufi
8 16 and Bendada 2008, Kadry and Smaili 2007, Lumini and Nanni 2007, Abate et al 2007, Sim et al 2007). Out of all application oriented biometrics, fingerprint based biometrics has gained much attention over the other approaches and finds a wide spectrum of applications in almost all disciplines of engineering due to various reasons. They are: It is believed to be unique for an individual. Imitation of more than one feature (minutiae, core point and delta points) for a particular fingerprint is not an easy task. The complexity involved in remembering the authentication tools like passwords and keys is nil. Accordingly many significant works have been carried out and algorithms have been developed for fingerprint classification, verification and compression. Each and every algorithm possesses certain/common limitations which make the algorithm fail to act as a unique biometric system. Some common observations noted from the literature are as follows: Each and every sensor captures fingerprints having different quality and image format. The quality of the fingerprint depends on the precision of the scanner or the device used for acquisition. The change of any particular image format to standard one needed for pre/post processing of a particular algorithm further trim down the enhancement in terms of image quality.
9 17 Inability of the existing algorithm to handle the poor/low quality images like the one available in FVC2002-DB1 database. Inability of the algorithm to categorize the fingerprints due to their low image quality observed with them. Exhaustive search time needed for fingerprint verification. Difficulty in combining the minutiae based details and the orientation angle to form a feature template. Inability of the algorithm to handle fingerprints which are having noisy background leading to yield inaccurate features and in turn increases the false acceptance rate (FAR) or reduces the false rejection rate (FRR) during classification and matching. Necessity of exhaustive storage space for housing the enrolled fingerprints in uncompressed format. In order to overcome the above limitations in this research, improved versions of a) Feedback based contour detector model for fingerprint classification b) Triangular matching and hamming distance method for fingerprint verification c) Local structure matching algorithm for fingerprint verification d) Enhanced versions of adaptive lifting scheme and adaptive polyphase sub-band decomposition structures for fingerprint image compression
10 18 are proposed and they are expected to yield accurate fingerprint classification and for verification with a minimum False Rejection Rate (FRR) and False Acceptance Rate (FAR) and a high quality compressed fingerprints. In addition, an embedded crypto-biometric authentication scheme for ATM banking systems is developed for user authentication. Further a multi-modal biometric system is introduced by combining the characteristics of fingerprint and iris in order to enhance the system security. 2.4 SUMMARY In this chapter, review of literature has been done in the field of fingerprint based biometrics. Some common drawbacks are listed and the need for the present work has been discussed. In the next chapter, the fingerprint technologies like fingerprint acquisition, fingerprint image classification, fingerprint authentication and its verification will be explained.
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