Biometrics and Bioinformatics

C. Anand ¹, R. Lawrance ²

Affiliations
1 Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi-626 124, Tamil Nadu, IN
2 Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi-626 124, Tamil Nadu, IN

Abstract

Face Recognition is a significant research area since it has plenty of application domains in pattern recognition, image processing, biometrics etc. Researchers contributed lot of algorithms and techniques to uncover the mask of face recognition arena. In our research, a Hidden Markov Model (HMM) based face recognition system along with Singular Value Decomposition (SVD) coefficients is discussed. Human face is divided into seven facial regions and a few quantized SVD Coefficients were trained to choose the facial features. Median Filtering is used as a preprocessing operation for efficient computation. Observation vectors are generated by dividing each face image into overlapping blocks and SVD coefficients acts as a base for constructing the observation sequence. Due to the discrete nature of HMM, quantization process is introduced to model the continuous observation vectors. The system is trained and tested on ORL face database consist of 400 images of 40 persons in Portable Gray Map (.pgm) format. Five face images of a person are considered for training the system and tested against 200 unseen faces. Our system achieves a recognition rate of 99.5% with a computational speed of 0.22 seconds per image. Choosing SVD coefficients as features increases the efficiency, in turn reducing the complexity. Experimental results revealed that our proposed system outperforms many of the traditional HMM based face recognition methods.

Keywords

Face Recognition, Hidden Markov Models (HMM), Median Filtering, Pattern Recognition, Singular Value Decomposition (SVD) Coefficients.

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K. Aruna Devi ¹, T. Marimuthu ¹, R. Lawrance ²

Affiliations
1 Computer Application, ANJA College, Sivakasi, Tamilnadu, IN
2 ANJA College, Sivakasi 626 124, Tamilnadu, IN

Abstract

The core objective of this paper is to analyze the Dengue Serotypes using online biological databases & tools and to generate a novel gracious tool to carry out sequence analysis using bioinformatics algorithms and concepts on Dengue virus sequences or other organisms that will be helpful in attaining knowledge for new invention. The Dengue virus is a member of the family Flavi virus. It is transmitted to people through the bite of the mosquitoes “Aedes aegypti" and “Aedes albopictus”. Dengue virus is now believed to be the most common anthropod-borne disease (an infectious disease carried by insect vectors) in the world and the Dengue fever is also called as “Break Bone Fever”. Dengue is mainly found in the tropics because the mosquitoes require a warm climate. A major fear of epidemiologists is that the mosquitoes will develop resistance to cooler climates and then be able to infect people living in any climate. Predicting the relationship between Dengue Serotypes will definitely help the Biotechnologists and Bioinformaticians to move one step forward in discovering vaccine for Dengue. For that the tool “Sequence Miner” created with computational intelligence is much helpful. Computational intelligence comprises practical adaptation concepts, paradigms, algorithms and implementations that enable or facilitate appropriate actions (intelligent behavior) in complex and changing environment.

Keywords

Dengue Serotype, Fasta, Phylogenetic Tree, Sequence Alignment.

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