Performance analysis of different matrix decomposition methods on face recognition

Applications using face biometric are ubiquitous in various domains. We propose an efficient method using Discrete Wavelet Transform (DWT), Extended Directional Binary codes (EDBC), three matrix decompositions and Singular Value Decomposition (SVD) for face recognition. The combined effect of Schur, Hessenberg and QR matrix decompositions are utilized with existing algorithm. The discrimination power between two different persons is justified using Average Overall Deviation (AOD) parameter. Fused EDBC and SVD features are considered for performance calculation. City-block and Euclidean Distance (ED) measure is used for matching. Performance is improved on YALE, GTAV and ORL face databases compared with existing methods

Optimized Face Recognition Algorithm using Spatial and Transform Domain Techniques

The biometrics is used to identify or verify persons effectively in the real time scenario. In this paper, we propose Optimized Face Recognition Algorithm using Spatial and Transform Domain Techniques. The face images are preprocessed using Discrete Wavelet Transform (DWT), resize and filtering. The Compound Local Binary Pattern (CLBF) is used to generate magnitude and sign components from preprocessed face images. The histogram is applied on sign and magnitude components of CLBF to compress number of features. The generated histogram features are concatenated to form CLBP-Histogram features. The Fast Fourier Transformation (FFT) is applied on preprocessed image and FFT magnitude features are generated. The CLBP-Histogram features are fused with FFT magnitude features to generate final feature set. The final feature sets of test image and data base images are compared using Euclidian Distance (ED) to recognise a person. It is observed that the performance parameter of the proposed algorithm is better compared to existing algorithms

2-Trifluoromethyl-10H-benzo4,5imidazo1,2-apyrimidin-4-one

In the molecule of the title compound, C11H6F3N3O, the three fused rings of the benzo4,5imidazo1,2-apyrimidine unit are essentially coplanar, the maximum deviation from the mean plane being 0.096 (2) à . In the crystal, N - Hâ¯O hydrogen bonds link the molecules into chains running along the b-axis direction

Conditional Entrench Spatial Domain Steganography

Steganography is a technique of concealing the secret information in a digital carrier media, so that only the authorized recipient can detect the presence of secret information. In this paper, we propose a spatial domain steganography method for embedding secret information on conditional basis using 1-Bit of Most Significant Bit (MSB). The cover image is decomposed into blocks of 8*8 matrix size. The first block of cover image is embedded with 8 bits of upper bound and lower bound values required for retrieving payload at the destination. The mean of median values and difference between consecutive pixels of each 8*8 block of cover image is determined to embed payload in 3 bits of Least Significant Bit (LSB) and 1 bit of MSB based on prefixed conditions. It is observed that the capacity and security is improved compared to the existing methods with reasonable PSNR

Graded Reliance Based Routing Scheme for Wireless Sensor Networks

In this paper Graded Reliance based routing algorithm is proposed to deal with defective nodes in Wireless Sensor Networks (WSN’s).The algorithm is intended to validated or build evidence that, by dynamically learning from previous experience and adapting the changes in the operational environment the application performance can be maximized and also enhance operative agility. Quality of service and social network measures are used to evaluate the confidence score of the sensor node. A dynamic model-based analysis is formulated for best reliance composition, aggregation, and formation to maximize routing performance. The results indicate that reliance based routing approaches yields better performance in terms of message delivery ratio and message delay without incurring substantial message overhead

Hybrid domain based face recognition using DWT, FFT and compressed CLBP

The characteristics of human body parts and behaviour are measured with biometrics, which are used to authenticate a person. In this paper, we propose Hybrid Domain based Face Recognition using DWT, FFT and Compressed CLBP. The face images are preprocessed to enhance sharpness of images using Discrete Wavelet Transform (DWT) and Laplacian filter. The Compound Local Binary Pattern (CLBP) is applied on sharpened preprocessed face image to compute magnitude and sign components. The histogram is applied on CLBP components to compress number of features. The Fast Fourier Transformation (FFT) is applied on preprocessed image and compute magnitudes. The histogram features and FFT magnitude features are fused to generate final feature. The Euclidian Distance (ED) is used to compare final features of test face images with data base face images to compute performance parameters. It is observed that the percentage recognition rate is high in the case of proposed algorithm compared to existing algorithms

Evolution of the Yukawa coupling constants and seesaw operators in the universal seesaw model

The general features of the evolution of the Yukawa coupling constants and seesaw operators in the universal seesaw model with det M_F=0 are investigated. Especially, it is checked whether the model causes bursts of Yukawa coupling constants, because in the model not only the magnitude of the Yukawa coupling constant (Y_L^u)_{33} in the up-quark sector but also that of (Y_L^d)_{33} in the down-quark sector is of the order of one, i.e., (Y_L^u)_{33} \sim (Y_L^d)_{33} \sim 1. The requirement that the model should be calculable perturbatively puts some constraints on the values of the intermediate mass scales and tan\beta (in the SUSY model).Comment: 21 pages, RevTex, 10 figure

Embedding information in DCT coefficients based on average covariance

Embedding information in DCT coefficients based on average covarianc

Fault Tolerance using Modified Hausdroff Distance Method

In wireless sensor and actor networks (WSANs), fault detection is of prime importance where an unexpected failure should be properly identified by the network system. Since the sensor nodes are prone to failure, fault tolerance should be seriously considered in many sensor network applications. In this paper, we propose Fault Tolerance using Modified Hausdroff Distance Method (FTMHD).The nodes are randomly deployed in an M*N area. The actor node verifies whether it receives correct data from node or not. The Modified Hausdroff Distance (MHD) is used to determine lower and upper bound of correct data range to be detected by the nodes. The proposed FTMHD has better sampling fraction and compression ratio compared to existing technique

Enhanced Topology Aware Routing for WSN

In this paper Enhanced Topology Aware Routing (ETAR) is proposed for point to point routing in wireless sensor network. Each node is characterized by a coordinate vector consisting of the shortest path hop distances to a subset of nodes, named anchors. The ETAR algorithm efficiently maps a network topology into a low-dimensional virtual coordinate space where hop distances between pairwise nodes are preserved. It assist greedy forwarding to find the right neighbor that is one hop closer to the destination by eliminating the local minimum problem and achieve high success ratio of packet delivery and throughput without location informatio