Informatics Publishing Limited

K. P. Mohammed Basheer ¹, K. Meena ², K. R. Subramanian ³

Affiliations
1 Department of Computer Applications, Shrimati Indira Gandhi College, Tiruchirappalli, Tamilnadu, IN
2 Bharathidasan University & Research Guide, IN
3 Department of Computer Applications, Shrimati Indira Gandhi College, Tiruchirappalli, IN

Abstract

With the huge development of broadband network, distribution of multimedia by means of Internet is an uncomplicated method of communication and data exchange. Intellectual Property (IP) protection is a vital component in a multimedia broadcast system. Traditional IP protection methods can be classified into two major categories: encryption and watermarking. Content protection has turned out to be one of the most considerable and demanding problems of this field. This paper proposes a multimedia content protection framework that is dependent on biometric data of the users, a layered encryption/decryption scheme and watermarking. Scalable encryption algorithms result from a transaction between implementation cost and resulting performances. In addition, this approach generally aims to be exploited competently on a large range of platforms. The computational necessities and applicability of the proposed method are addressed. By utilizing the benefit of the nature of cryptographic schemes and digital watermarking, the copyright of multimedia contents can be protected. In this paper, the scalable transmission technique is utilized over the broadcasting environment for encryption. The embedded watermark can be thus extracted with high confidence.

Keywords

Multimedia, Security, Biometrics, Watermarking, Scalable Encryption.

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K. Meena , M. Manimekalai

Abstract

One of the most significant problems in biomedical research today is the prediction of protein structure from knowledge of the primary amino acid sequence. Secondary Structure Prediction (SSP) is a very typical problem in the field of bioinformatics.Prediction of secondary structure of Proteins can be done from the Protein sequence. In the Protein structure prediction, the Amino Acid sequence of a Protein, the so-called primary structure, can be easily determined from the sequence on the Gene that codes for it. This primary structure exclusively determines a structure in its native environment. Thus primary structure plays a key role in understanding the function of the Protein. Majority of the previous research have ignored the influence of residue conformational preference on structure prediction of proteins. The primary focus of this research is to investigate a variety of approaches for employing ANN and Machine Learning techniques in order to predict the secondary structure of proteins in soybeans.

Keywords

Protein Structure Prediction, RBFNN, MELM, SVM, Amino Acid, Soybeans.

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K. Meena ¹, M. Manimekalai ²

Affiliations
1 Shrimati Indira Gandhi College, Trichy, IN
2 Dept. of IT & Applications, Shrimati Indira Gandhi College, Trichy, IN

Abstract

Proteins are a definite kind of biological macromolecules that is present in all biological organisms. Amino acids are the building blocks of proteins. They are primary structure, secondary structure, tertiary structure and quaternary structure. Most of the existing algorithms for predicting the content of the protein secondary structure elements have been based on the conventional amino acid composition, where no sequence coupling effects are taken into consideration. Prediction of three dimensional structure, secondary structure, and functional sites of proteins from primary structure are the three major problems in structural bioinformatics. More than a few different approaches have been previously used in these kind of predictions among which, artificial neural networks have been of great interest due to their capability of learning from observations and prediction of the structures for non classified instances. This paper proposes a technique for prediction of protein structure in soybeans using neural networks. This paper uses RBFNN in order to predict the secondary structure. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. The neural network architecture used in our approach is a feed forward and fully connected neural network whose Gaussian centers are optimized by genetic algorithm. Experimental are carried on dataset obtained from Protein Data Bank (PDB) to predict the structure of the protein present in it.

Keywords

Amino Acids (AA), Bioinformatics, Protein Structure Prediction, Secondary Structure Content, Neural Networks, Radial Basis Function Neural Networks (RBFNN), Genetic Algorithm (GA), Protein Data Bank (PDB).

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R. Reena Rose ¹, A. Suruliandi ², K. Meena ³

Affiliations
1 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
3 Department of Electronics and Communication Engineering, J. P. College of Engineering, IN

Abstract

Texture descriptors have an important role in recognizing face images. However, almost all the existing local texture descriptors use nearest neighbors to encode a texture pattern around a pixel. But in face images, most of the pixels have similar characteristics with that of its nearest neighbors because the skin covers large area in a face and the skin tone at neighboring regions are same. Therefore this paper presents a general framework called Local Texture Description Framework that uses only eight pixels which are at certain distance apart either circular or elliptical from the referenced pixel. Local texture description can be done using the foundation of any existing local texture descriptors. In this paper, the performance of the proposed framework is verified with three existing local texture descriptors Local Binary Pattern (LBP), Local Texture Pattern (LTP) and Local Tetra Patterns (LTrPs) for the five issues viz. facial expression, partial occlusion, illumination variation, pose variation and general recognition. Five benchmark databases JAFFE, Essex, Indian faces, AT & T and Georgia Tech are used for the experiments. Experimental results demonstrate that even with less number of patterns, the proposed framework could achieve higher recognition accuracy than that of their base models.

Keywords

Face Recognition, Local Texture Description Framework, Nearest Neighborhood Classification, Chi-Square Distance Metric.

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A. Suruliandi ¹, R. Reena Rose ², K. Meena ³

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN
3 Department of Computer Science and Engineering, Sardar Raja College of Engineering, IN

Abstract

Texture feature plays a predominant role in recognizing face images. However different persons can have similar texture features that may degrade the system performance. Hence in this paper, the problem of face similarity is addressed by proposing a solution which combines textural and geometrical features. An algorithm is proposed to combine these two features. Five texture descriptors and few geometrical features are considered to validate the proposed system. Performance evaluations of these features are carried out independently and jointly for three different issues such as expression variation, illumination variation and partial occlusion with objects. It is observed that the combination of textural and geometrical features enhance the accuracy of face recognition. Experimental results on Japanese Female Facial Expression (JAFFE) and ESSEX databases indicate that the texture descriptor Local Binary Pattern achieves better recognition accuracy for all the issues considered.

Keywords

Face Recognition, Texture Features, Geometric Features, Nearest Neighborhood Classification, Chi-Square Distance Metric.

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K. Meena ¹, A. Suruliandi ², R. Reena Rose ³

Affiliations
1 Department of Electronics and Communication Engineering, J. P. College of Engineering, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
3 Department of Computer Applications, St. Xavier’s Catholic College of Engineering, IN

Abstract

Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE), Gamma Intensity Correction (GIC), Normalization chain and Modified Homomorphic Filtering (MHF) are used for preprocessing. Owing to great success, the texture features are commonly used for face recognition. But these features are severely affected by lighting changes. Hence texture based models Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Local Texture Pattern (LTP) and Local Tetra Patterns (LTrPs) are experimented under different lighting conditions. In this paper, illumination invariant face recognition technique is developed based on the fusion of illumination preprocessing with local texture descriptors. The performance has been evaluated using YALE B and CMU-PIE databases containing more than 1500 images. The results demonstrate that MHF based normalization gives significant improvement in recognition rate for the face images with large illumination conditions.

Keywords

Face Recognition, Texture Analysis, Texture Features.

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T. Chakravarthy ¹, K. Meena ², D. Nathiya ¹

Affiliations
1 A. V. V. M. Sri Pushpam College, Bharathidasan University, Tamil Nadu, IN
2 Shrimati Indira Gandhi College, Bharathidasan University, Tamil Nadu, IN

Abstract

Fingerprint classification based on statistical and structural (RNN and SVM) approach. RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in this support vector machine. SVMs are combined with a new error correcting codes scheme. This approach has two main advantages. (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) It can effectively identify the most difficult fingerprint images in the test set. In this experiment on the fingerprint database NIST-4 (National Institute of Science and Technology), our best classification accuracy of 94.7% is obtained by training SVM on both fingerCode and RNN -extracted futures of segmentation algorithm which has used very sophisticated "region growing process".

Keywords

Support Vector Machine, Recursive Neural Network, Region Growing, Error Correction Code.

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