Digital Image Processing

P. Hari Krishnan ¹, P. Ramamoorthy ²

Affiliations
1 Anna University of Technology, Coimbatore, IN
2 Sri Sakthi Institute of Engineering and Technology, Coimbatore, IN

Abstract

Image processing plays an important role in medical field because of its capability. Particularly, image segmentation offer several guides in medical field for analyzing the captured image. Usually, the medical images are captured via different medical image acquisition techniques. The captured image may be affected by noise because of some faults in the capturing devise; this will leads to false diagnosis. This paper focuses on enhancing the captured brain image by using image segmentation technique. Usually, brain image is captured using Magnetic Resonance Imaging (MRI) technique. The captured brain image will have high amount of noise or distortion, this noise must be removed before it is used for diagnosis purpose. Brain segmentation is widely applied for removing those noises to produce the clear image. The segmentation can be achieved with the help of clustering techniques. The widely used clustering technique for brain image segmentation technique is Fuzzy C-Means (FCM) clustering. But FCM will result in poor segmentation when more edge regions are involved. To overcome this problem, Fuzzy Possibilistic C-Means Algorithm (FPCM) is introduces. Even FPCM will result in poor segmentation when more noise are involved. To overcome all these problems, a Modified Fuzzy Possibilistic C-Means Algorithm (MFPCM) is proposed in this paper. The experimental result show that the segmentation resulted for the proposed technique is better when compared to the existing methods.

Keywords

Brain Image Segmentation, Fuzzy C-Means, Fuzzy Possibilistic C-Means, Modified Fuzzy Possibilistic C-Means.

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R. Gayathri ¹, P. Ramamoorthy ²

Affiliations
1 Vel Tech Dr. RR & Dr. SR Technical University, Chennai, IN
2 Sri Shakthi Engineering College, IN

Abstract

Unimodal biometric systems have to contend with a variety of problems such as noisy data, intraclass variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidence presented by multiple sources of information. Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacian face based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods. This paper discusses the various scenarios that are possible to improve the performance of multimodal biometric systems using the combined characteristics such as iris and face, the level of fusion (score level fusion) is applied to that are possible and the integration strategies that can be adopted in order to increase the overall system performance.

Keywords

Biometric, Multimodal, Score Level Fusion.

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M. Suganthy ¹, P. Ramamoorthy ²

Affiliations
1 Anna University, IN
2 Sri Sakthy Institute of Engineering and Technology, Coimbatore, IN

Abstract

Biometrics is the science and technology of measuring and analyzing biological data of human body, extracting a feature set from the acquired data, and comparing this set against to the template set in the database. Biometric systems have now been deployed in various commercial, civilian and forensic applications as a means of establishing identity. The biometric system is one such that can provide accurate and reliable scheme for person verification. Biometric systems are of two types: unimodal and multimodal.:Unimodal Biometric systems based solely on one-modal biometrics are often not able to meet the desired performance requirements for large user population applications, due to problems such as noisy data, intra-class variations, restricted degrees of freedom, non-university, spoof attacks, and unacceptable error rates. Multimodal biometrics refers to the use of a combination of two or more biometric modalities in a single identification system. The multimodal biometrics are; face and finger print, face and iris/, iris and finger print etc. In this paper we have concentrated in fusion of face and iris for multimodal biometric authentication. We adopted a new approach called the contourlet transform is a new extension of the wavelet transform in two dimensions using multi-scale and directional filter banks for iris feature extraction.

Keywords

Multimodal, Authentication, Fusion, Face, Iris, Contourlet, Wavelet.

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