Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks

Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm

Coherent steganography using Segmentation and DCT

The important issue of modern communication is establishing secret communication while using public channel and is achieved by steganography. In this paper, we propose Coherent Steganographic Technique using Segmentation and Discrete Cosine Transform (CSSDCT). The cover image is divided into 8*8 blocks and DCT is applied on each block. The number of payload MSB bits is embedded into DCT coefficients of the cover image coherently based on the values of DCT coefficients. It is observed that the proposed algorithm has better PSNR, Security and capacity compared to the existing techniques. © 2010 IEEE

Biometric Security System Based on Signature Verification Using Neural Networks

The signature verification is the behavioral parameter of biometrics and is used to authenticate a person. A typical signature verification system generally consists of four components: data acquisition, preprocessing, feature extraction and verification. In this paper, Biometric Security System Based on Signature Verification Using Neural Networks (BSSV) is presented. The global and grid features are combined to generate new set of features for the verification of signature. The Neural Network is used as a classifier for the authentication of a signature. The performance analysis is verified on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithms. © 2010 IEEE

Integration of Spatial and Transform Domain in LSB Steganography

In this paper, we propose an integration of spatial and transform domains in LSB steganography (ISTDLS) where both the cover image as well as payload are divided into two cells each and RGB components of cell-1 are separated. The extracted RGB components of cover image cell-1 are individually transformed from spatial to transform domain using FFT/DCT/DWT and embedded differently, the components of cell-2 are retained in spatial domain itself

Dual transform technique for robust steganographya

Steganography enables to have a secret communication in modern information technology using public channel. In this paper, we propose Dual Transform Technique for Robust Steganography (DTTRS). The cover image is segmented into blocks of 4*4 each and Discrete Wavelet Transform (DWT) is applied on each block. In the resulting DWT coefficients, blocks of vertical band of 2*2 each are considered and Integer Wavelet Transform (IWT) is applied to get blocks of 1*1 each. The IWT is applied on vertical band of DWT of payload to generate coefficients of payload and are embedded into IWT coefficients of cover image using least significant bit replacement method. On applying IIWT and IDWT, stego image is derived. The concept of error detecting and correcting coding technique is employed to ensure more reliable communication. It is observed that the proposed algorithm has excellent PSNR, provides high level security and more robust compared to individual transform techniques. © 2011 IEEE

Hybrid fingerprint matching using block filter and strength factors

Fingerprint verification is a socially accepted biometric method for identification of a person. In this paper we propose Hybrid Fingerprint Matching using Block Filter and Strength Factor (HFMBFS). The minutiae and ridge based methods are combined to verify the fingerprint matching using strength factors Alpha (α) and Beta (β). For minutiae and ridge extraction Block Filter and Hough Transform are used respectively. It is observed that the matching percentage of two different fingerprints is improved compared to the existing algorithms. © 2010 IEEE

Combined Off-Line Signature Verification Using Neural Networks

In this paper, combined off-line signature verification using Neural Network (CSVNN) is presented. The global and grid features are combined to generate new set of features for the verification of signature. The Neural Network (NN) is used as a classifier for the authentication of a signature. The performance analysis is verified on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm. © Springer-Verlag Berlin Heidelberg 2010

Iris recognition based on DWT and PCA

An Iris is unique compared to all other biometric and is used to authenticate a person. In this paper, we propose iris recognition based on DWT and PCA (IRDP). The iris region is localized using morphological process and a template is formed by using the left and right part of the iris to the pupil by discarding the upper and lower parts of the iris as they are usually occluded by eyelids and eyelashes. The DWT is applied on the histogram of iris template. The features are generated from the approximation band of the DWT using PCA. The classifiers viz., KNN, RF and SVM are used for matching. It is observed that the proposed algorithm has better performance parameters compared to existing algorithm. © 2011 IEEE