Development of a Biometric Personal Authentication System Based on Fingerprint and Speech

Biometrics deals with the physiological and behavioral characteristics of an individual to establish identity. Fingerprint based authentication is the most advanced biometric authentication technology. The minutiae based fingerprint identification method offer reasonable identification rate. The feature minutiae map consists of about 70-100 minutia points and matching accuracy is dropping down while the size of database is growing up. Hence it is inevitable to make the size of the fingerprint feature code to be as smaller as possible so that identification may be much easier. In this research, a novel global singularity based fingerprint representation is proposed. Fingerprint baseline, which is the line between distal and intermediate phalangeal joint line in the fingerprint, is taken as the reference line. A polygon is formed with the singularities and the fingerprint baseline. The feature vectors are the polygonal angle, sides, area, type and the ridge counts in between the singularities. 100% recognition rate is achieved in this method. The method is compared with the conventional minutiae based recognition method in terms of computation time, receiver operator characteristics (ROC) and the feature vector length. Speech is a behavioural biometric modality and can be used for identification of a speaker. In this work, MFCC of text dependant speeches are computed and clustered using k-means algorithm. A backpropagation based Artificial Neural Network is trained to identify the clustered speech code. The performance of the neural network classifier is compared with the VQ based Euclidean minimum classifier. Biometric systems that use a single modality are usually affected by problems like noisy sensor data, non-universality and/or lack of distinctiveness of the biometric trait, unacceptable error rates, and spoof attacks. Multifinger feature level fusion based fingerprint recognition is developed and the performances are measured in terms of the ROC curve. Score level fusion of fingerprint and speech based recognition system is done and 100% accuracy is achieved for a considerable range of matching threshold

Molecular characterization of a crustinlike antimicrobial peptide in the giant tiger shrimp, Penaeus monodon, and its expression profile in response to various immunostimulants and challenge with WSSV

A crustinlike antimicrobial peptide from the haemocytes of giant tiger shrimp, Penaeus monodon was partially characterized at the molecular level and phylogenetic analysis was performed. The partial coding sequence of 299 bp and 91 deduced amino acid residues possessed conserved cysteine residues characteristic of the shrimp crustins. Phylogenetic tree and sequence comparison clearly confirmed divergence of this crustinlike AMP from other shrimp crustins. The differential expression of the crustinlike AMP in P. monodon in response to the administration of various immunostimulants viz., two marine yeasts (Candida haemulonii S27 and Candida sake S165) and two bglucan isolates (extracted from C. haemulonii S27 and C. sake S165) were noted during the study. Responses to the application of two grampositive probiotic bacteria (Bacillus MCCB101 and Micrococcus MCCB104) were also observed. The immune profile was recorded preand postchallenge white spot syndrome virus (WSSV) by semiquantitative RTPCR. Expressions of seven WSSV genes were also observed for studying the intensity of viral infection in the experimental animals. The crustinlike AMP was found to be constitutively expressed in the animal and a significant downregulation could be noted postchallenge WSSV. Remarkable downregulation of the gene was observed in the immunostimulant fed animals prechallenge followed by a significant upregulation postchallenge WSSV. Tissuewise expression of crustinlike AMP on administration of C. haemulonii and Bacillus showed maximum transcripts in gill and intestine. The marine yeast, C. haemulonii and the probiotic bacteria, Bacillus were found to enhance the production of crustinlike AMP and confer significant protection to P. monodon against WSSV infection

Molecular characterization of a crustin-like, putative antimicrobial peptide, Fi-crustin, from the Indian white shrimp, Fenneropenaeus indicus

Fish & Shellfish Immunology 28 (2010) 216-220

Molecular characterization and phylogenetic analysis of a penaeidin-like antimicrobial peptide, Fi-penaeidin from Fenneropenaeus indicus

Antimicrobial peptides (AMPs) play a major role in innate immunity. Penaeidins are a family of AMPs that appear to be expressed in all penaeid shrimps. Penaeidins are composed of an N-terminal proline-rich domain, followed by a C-terminal domain containing six cysteine residues organized in two doublets. This study reports the first penaeidin AMP sequence, Fi-penaeidin (GenBank accession number HM243617) from the Indian white shrimp, Fenneropenaeus indicus. The full length cDNA consists of 186 base pairs encoding 61 amino acidswith an ORF of 42 amino acids and contains a putative signal peptide of 19 amino acids. Comparison of F. indicus penaeidin (Fi-penaeidin) with other known penaeidins showed that it shared maximum similarity with penaeidins of Farfantepenaeus paulensis and Farfantepenaeus subtilis (96% each). Fi-penaeidin has a predicted molecular weight (MW) of 4.478 kDa and theoretical isoelectric point (pI) of 5.3

A Novel Isoform of the Hepatic Antimicrobial Peptide, Hepcidin (Hepc-CB1), from a Deep-Sea Fish, the Spinyjaw Greeneye Chlorophthalmus bicornis (Norman, 1939): Molecular Characterisation and Phylogeny

Hepcidin is cysteine-rich short peptide of innate immune system of fishes, equipped to perform prevention and proliferation of invading pathogens like bacteria and viruses by limiting iron availability and activating intracellular cascades. Hepcidins are diverse in teleost fishes, due to the varied aquatic environments including exposure to pathogens, oxygenation and iron concentration. In the present study, we report a 87-amino acid (aa) preprohepcidin (Hepc-CB1) with a signal peptide of 24 aa, a prodomain of 39 aa and a bioactive mature peptide of 24 aa from the gill mRNA transcripts of the deep-sea fish spinyjaw greeneye, Chlorophthalmus bicornis. Molecular characterisation and phylogenetic analysis categorised the peptide to HAMP2-like group with a mature peptide of 2.53 kDa; a net positive charge (?3) and capacity to form b-hairpin-like structure configured by 8 conserved cysteines. The present work provides new insight into the mass gene duplication events and adaptive evolution of hepcidin isoforms with respect to environmental influences and positive Darwinian selection. This work reports a novel hepcidin isoform under the group HAMP2 from a nonacanthopterygian deep-sea fish, C. bicornis

Molecular Characterisation and Phylogenetic Analysis of a Novel Isoform of Hepatic Antimicrobial Peptide, Hepcidin (Zc-hepc1), from the Coral Fish Moorish idol, Zanclus cornutus (Linnaeus,1758)

Hepcidin is a family of short cysteine-rich antimicrobial peptides (AMPs) participating in various physiological functions with inevitable role in host immune responses. Present study deals with identification and characterisation of a novel hepcidin isoform from coral fish Zanclus cornutus. The 81 amino acid (aa) preprohepcidin obtained from Z. cornutus consists of a hydrophobic aa rich 22 mer signal peptide, a highly variable proregion of 35 aa and a bioactive mature peptide with 8 conserved cysteine residues which contribute to the disulphide back bone. The mature hepcidin, Zc-hepc1 has a theoretical isoelectric point of 7.46, a predicted molecular weight of 2.43 kDa and a net positive charge of ?1. Phylogenetic analysis grouped Z. cornutus hepcidin with HAMP2 group hepcidins confirming the divergent evolution of hepcidin-like peptide in fishes. Zc-hepc1 can attain a b-hairpin-like structure with two antiparallel b-sheets. This is the first report of an AMP from the coral fish Z. cornutus.

An Improved Technique for Evolving Wavelet Coefficients for Fingerprint Image Compression

In this paper, an improved technique for evolving wavelet coefficients refined for compression and reconstruction of fingerprint images is presented. The FBI fingerprint compression standard [1, 2] uses the cdf 9/7 wavelet filter coefficients. Lifting scheme is an efficient way to represent classical wavelets with fewer filter coefficients [3, 4]. Here Genetic algorithm (GA) is used to evolve better lifting filter coefficients for cdf 9/7 wavelet to compress and reconstruct fingerprint images with better quality. Since the lifting filter coefficients are few in numbers compared to the corresponding classical wavelet filter coefficients, they are evolved at a faster rate using GA. A better reconstructed image quality in terms of Peak-Signal-to-Noise-Ratio (PSNR) is achieved with the best lifting filter coefficients evolved for a compression ratio 16:1. These evolved coefficients perform well for other compression ratios also.

Faster techniques to evolve wavelet coefficients for better fingerprint image compression

In this article, techniques have been presented for faster evolution of wavelet lifting coefficients for fingerprint image compression (FIC). In addition to increasing the computational speed by 81.35%, the coefficients performed much better than the reported coefficients in literature. Generally, full-size images are used for evolving wavelet coefficients, which is time consuming. To overcome this, in this work, wavelets were evolved with resized, cropped, resized-average and cropped-average images. On comparing the peak- signal-to-noise-ratios (PSNR) offered by the evolved wavelets, it was found that the cropped images excelled the resized images and is in par with the results reported till date. Wavelet lifting coefficients evolved from an average of four 256 256 centre-cropped images took less than 1/5th the evolution time reported in literature. It produced an improvement of 1.009 dB in average PSNR. Improvement in average PSNR was observed for other compression ratios (CR) and degraded images as well. The proposed technique gave better PSNR for various bit rates, with set partitioning in hierarchical trees (SPIHT) coder. These coefficients performed well with other fingerprint databases as well.

Evolution of Better Wavelet Coefficients for Fingerprint Image Compression using cropped images

This paper explains the Genetic Algorithm (GA) evolution of optimized wavelet that surpass the cdf9/7 wavelet for fingerprint compression and reconstruction. Optimized wavelets have already been evolved in previous works in the literature, but they are highly computationally complex and time consuming. Therefore, in this work, a simple approach is made to reduce the computational complexity of the evolution algorithm. A training image set comprised of three 32x32 size cropped images performed much better than the reported coefficients in literature. An average improvement of 1.0059 dB in PSNR above the classical cdf9/7 wavelet over the 80 fingerprint images was achieved. In addition, the computational speed was increased by 90.18 %. The evolved coefficients for compression ratio (CR) 16:1 yielded better average PSNR for other CRs also. Improvement in average PSNR was experienced for degraded and noisy images as well

Multifinger Feature Level Fusion Based Fingerprint Identification

Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Feature level fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification