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

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

A Study on the Quality Attributes of Kerala as a Global Tourism Destination-2005

The study was carried out with the broad objective to understand the quality attributes of Kerala as a global tourism destination. It also sheds some light on the nature of international travel market for Kerala in terms of activities , benefit sought , country and trip profile. For understanding the difference in level of tourists perception , the study also tried to compare overall trip satisfaction and impression with destination for different tourists groups categorized into country of origin and various socio-demographic groups.

Global Value Chain Linkages of Manufacturing Export Firms: A Study of The Kannur Home Textile Industry

The literature on the involvement of developing countries in trade has focused on the effects of different aspects of globalization on firms, regions and countries. The study attempts to examine how an export based industry, locallyembedded and originated on the basis of regional strengths has been inserted into the global trade framework. Though the unit of analysis is the manufacturing export firm in the region of Kannur, it represents the entire home textile export industry from the state of Kerala, as close to 90% of fabric exports in home furnishing material, textiles for upholstery and decoration and stitched or fused, and branded made ups are from the region. From a global perspective, how developing countries face newer trade restrictions and overcome non quota barriers by firm and region specific activities within a value chain framework is a major research area, which has already contributions from the Ludhiana woolen cluster (Tewari,1999 ) and the Tirupur cluster in India (Cawthorne, 1995). The study contributes to the value chain literature by examining the governance and upgrading as well as how firms benefit from linkages. India has a number of export oriented agglomerations or regions where firms have been serving export markets for many years. In many cases it is no longer the supply side policy actions that determine how they are able to penetrate new markets or expand existing market share. Based on this study it becomes possible to understand how the global value chain operates in these different industries to examine whether there is a danger of immiserisation of growth or low road growth

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

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

Fishery Commodity Chain Trap vis-à-vis Global Quality Standards: An Analysis of the Kerala Marine Fishery

The article examines the commodity chain trap of marine fishery in Kerala, at both material and value terms, and its ramifications in the globalised fishery chains. The marketing chains both material and value, are very complex in nature since they involve many types of markets and large number of intermediaries and participants. The article also scrutinizes the sensitivity of consumers’ and country’s responses in terms of dietary and hygienic standards relating to seafood trade. In addition, it discusses the devastating effect about the recent stipulations like the US Bio- Terrorism Act and Shrimp anti-dumping duty on the Kerala fishery products