Palm Print Based Person Identification

Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.

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Palm-Print Pattern Matching Based on Features Using Rabin-Karp for Person Identification

The Scientific World JOURNAL ◽

10.1155/2015/382697 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

S. Kanchana ◽

G. Balakrishnan

Keyword(s):

Pattern Matching ◽

Feature Matching ◽

Individual Identification ◽

Person Identification ◽

Training Samples ◽

Matching Efficiency ◽

Finite Set ◽

Feature Based ◽

Matching Procedure ◽

Palm Print

Palm-print based individual identification is regarded as an effectual method for identifying persons with high confidence. Palm-print with larger inner surface of hand contains many features such as principle lines, ridges, minutiae points, singular points, and textures. Feature based pattern matching has faced the challenge that the spatial positional variations occur between the training and test samples. To perform effective palm-print features matching, Rabin-Karp Palm-Print Pattern Matching (RPPM) method is proposed in this paper. With the objective of improving the accuracy of pattern matching, double hashing is employed in RPPM method. Multiple patterns of features are matched using the Aho-Corasick Multiple Feature matching procedure by locating the position of the features with finite set of bit values as an input text, improving the cumulative accuracy on hashing. Finally, a time efficient bit parallel ordering presents an efficient variation on matching the palm-print features of test and training samples with minimal time. Experiment is conducted on the factors such as pattern matching efficiency rate, time taken on multiple palm-print feature matching efficiency, and cumulative accuracy on hashing.

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Human Ear Pattern Recognition System

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.35 ◽

2017 ◽

Vol 7 (8) ◽

pp. 117 ◽

Cited By ~ 1

Author(s):

V. Jagan Naveen ◽

K. Krishna Kishore ◽

P. Rajesh Kumar

Keyword(s):

Pattern Recognition ◽

Expectation Maximization Algorithm ◽

Recognition System ◽

Modern World ◽

Person Identification ◽

Biometric Systems ◽

Different Types ◽

Recognition Systems ◽

Human Ear ◽

Pca Algorithm

In the modern world, human recognition systems play an important role to improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm. Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

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