The development of automated palmprint identification using major flexion creases

Palmar flexion crease matching is a method for verifying or establishing identity. New methods of palmprint identification, that complement existing identification strategies, or reduce analysis and comparison times, will benefit palmprint identification communities worldwide. To this end, this thesis describes new methods of manual and automated palmar flexion crease identification, that can be used to identify palmar flexion creases in online palmprint images. In the first instance, a manual palmar flexion crease identification and matching method is described, which was used to compare palmar flexion creases from 100 palms, each modified 10 times to mimic some of the types of alterations that can be found in crime scene palmar marks. From these comparisons, using manual palmar flexion crease identification, results showed that when labelled within 10 pixels, or 3.5 mm, of the palmar flexion crease, a palmprint image can be identified with a 99.2% genuine acceptance rate and a 0% false acceptance rate. Furthermore, in the second instance, a new method of automated palmar flexion crease recognition, that can be used to identify palmar flexion creases in online palmprint images, is described. A modified internal image seams algorithm was used to extract the flexion creases, and a matching algorithm, based on kd-tree nearest neighbour searching, was used to calculate the similarity between them. Results showed that in 1000 palmprint images from 100 palms, when compared to manually identified palmar flexion creases, a 100% genuine acceptance rate was achieved with a 0.0045% false acceptance rate. Finally, to determine if automated palmar flexion crease recognition can be used as an effective method of palmprint identification, palmar flexion creases from two online palmprint image data sets, containing images from 100 palms and 386 palms respectively, were automatically extracted and compared. In the first data set, that is, for images from 100 palms, an equal error rate of 0.3% was achieved. In the second data set, that is, for images from 386 palms, an equal error rate of 0.415% was achieved.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Dual-tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram Method for Palmprint Recognition

In the paper, we improve the Local Binary Pattern Histogram (LBPH) approach and combine it with Dual-Tree Complex Wavelet Transform (DT-CWT) to propose a Dual-Tree Complex Wavelet Transform based Local Binary Pattern Weighted Histogram (DT-CWT based LBPWH) method for palmprint representation and recognition. The approximate shift invariant property of the DT-CWT and its good directional selectively in 2D make it a very appealing choice for palmprint representation. LBPH is a powerful texture description method, which considers both shape and texture information to represent an image. To enhance the representation capability of LBPH, a weight set is computed and assigned to the finial feature histogram. Here we needn't construct a palmprint model by a train sample set, which is not like some methods based on subspace discriminant analysis or statistical learning. In the approach, a palmprint image is first decomposed into multiple subbands by using DT-CWT. After that, each subband in complex wavelet domain is divided into non-overlapping sub-regions. Then LBPHs are extracted from each sub-region in each subband, and lastly, all of LBPHs are weighted and concatenated into a single feature histogram to effectively represent the palmprint image. A Chi square distance is used to measure the similarity of different feature histograms and the finial recognition is performed by the nearest neighborhood classifier. A group of optimal parameters is chosen by 20 verification tests on our palmprint database. In addition, the recognition results on our palmprint database and the database from the Hong Kong Polytechnic University show the proposed method outperforms other methods

Palmprint Verification Using Time Series Method

The use of biometrics as an automatic recognition system is growing rapidly in solving security problems; palmprint is one of biometric system which often used. This paper used two steps in center of mass moment method for region of interest (ROI) segmentation and apply the time series method combined with block window method as feature representation. Normalized Euclidean Distance is used to measure the similarity degrees of two feature vectors of palm print. System testing is done using 500 samples palms, with 4 samples as the reference image and the 6 samples as test images. Experiment results show this system can achieve a high performance with success rate about 97.33% (FNMR=1.67%, FMR=1.00 %, T=0.036)

Palmprint Verification Using Time Series Method

The use of biometrics as an automatic recognition system is growing rapidly in solving security problems; palmprint is one of biometric system which often used. This paper used two steps in center of mass moment method for region of interest (ROI) segmentation and apply the time series method combined with block window method as feature representation. Normalized Euclidean Distance is used to measure the similarity degrees of two feature vectors of palm print. System testing is done using 500 samples palms, with 4 samples as the reference image and the 6 samples as test images. Experiment results show this system can achieve a high performance with success rate about 97.33% (FNMR=1.67%, FMR=1.00 %, T=0.036)

Biometric recognition based on the texture along palmprint lines

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Palmprint Identification Based on Generalization of IrisCode

The development of accurate and reliable security systems is a matter of wide interest, and in this context biometrics is seen as a highly effective automatic mechanism for personal identification. Among biometric technologies, IrisCode developed by Daugman in 1993 is regarded as a highly accurate approach, being able to support real-time personal identification of large databases. Since 1993, on the top of IrisCode, different coding methods have been proposed for iris and fingerprint identification. In this research, I extend and generalize IrisCode for real-time secure palmprint identification. PalmCode, the first coding method for palmprint identification developed by me in 2002, directly applied IrisCode to extract phase information of palmprints as features. However, I observe that the PalmCodes from the different palms are similar, having many 45o streaks. Such structural similarities in the PalmCodes of different palms would reduce the individuality of PalmCodes and the performance of palmprint identification systems. To reduce the correlation between PalmCodes, in this thesis, I employ multiple elliptical Gabor filters with different orientations to compute different PalmCodes and merge them to produce a single feature, called Fusion Code. Experimental results demonstrate that Fusion Code performs better than PalmCode. Based on the results of Fusion Code, I further identify that the orientation fields of palmprints are powerful features. Consequently, Competitive Code, which uses real parts of six Gabor filters to estimate the orientation fields, is developed. To embed the properties of IrisCode, such as high speed matching, in Competitive Code, a novel coding scheme and a bitwise angular distance are proposed. Experimental results demonstrate that Competitive Code is much more effective than other palmprint algorithms. Although many coding methods have been developed based on IrisCode for iris and palmprint identification, we lack a detailed analysis of IrisCode. One of the aims of this research is to provide such analysis as a way of better understanding IrisCode, extending the coarse phase representation to a precise phase representation, and uncovering the relationship between IrisCode and other coding methods. This analysis demonstrates that IrisCode is a clustering process with four prototypes; the locus of a Gabor function is a two-dimensional ellipse with respect to a phase parameter and the bitwise hamming distance can be regarded as a bitwise angular distance. In this analysis, I also point out that the theoretical evidence of the imposter binomial distribution of IrisCode is incomplete. I use this analysis to develop a precise phase representation which can enhance iris recognition accuracy and to relate IrisCode and other coding methods. By making use of this analysis, principal component analysis and simulated annealing, near optimal filters for palmprint identification are sought. The near optimal filters perform better than Competitive Code in term of d’ index. Identical twins having the closest genetics-based relationship are expected to have maximum similarity in their biometrics. Classifying identical twins is a challenging problem for some automatic biometric systems. Palmprint has been studied for personal identification for many years. However, genetically identical palmprints have not been studied. I systemically examine Competitive Code on genetically identical palmprints for automatic personal identification and to uncover the genetically related palmprint features. The experimental results show that the three principal lines and some portions of weak lines are genetically related features but our palms still contain rich genetically unrelated features for classifying identical twins. As biometric systems are vulnerable to replay, database and brute-force attacks, such potential attacks must be analyzed before they are massively deployed in security systems. I propose projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system based on Competitive Code. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks. In addition to brute-force attacks, I address the other three security issues: template re-issuances, also called cancellable biometrics, replay attacks, and database attacks. A random orientation filter bank (ROFB) is used to generate cancellable Competitive Codes for templates re-issuances. Secret messages are hidden in templates to prevent replay and database attacks. This technique can be regarded as template watermarking. A series of analyses is provided to evaluate the security levels of the measures