The School of Science and Technology of 'Yashwantrao Chavan Maharashtra Open University (YCMOU)' has proposed to offer 'Web Based Live Teaching Learning Support' from 'real' teacher, with 'Live Virtual Online Class (LVOC)' integrated with 'Learning Management System (LMS)' for all courses of all programmes on offer. In the first phase, school has started LVOC for total ten courses in from Feb 2014. This web-based system is designed to provide an opportunity to:Ø maximize interaction, discussion and spontaneous exchanges with 'real' teacher during live virtual class Ø present quality learning material to individual to suit his/her learning styles, interests, needs, and at their own pace.Further, LVOC is integrated with LMSs to present a set of features designed to provide an effective continuous assessment. The strategies adopted to provide learning support with guidance at every step of the way is elaborated here. In the next phase, school is planning to launch 'Online Certificate Course' for which all planned LVOC are already developed. As Learning is a collaborative process, authors have suggested additional strategies to be incorporated by 'real teacher' to offer 'Online Course'. This will help to ensure better quality and to develop confidence, comfort, and experience in online teaching.

<abstract><p>For reliable and accurate multimodal biometric based person verification, demands an effective discriminant feature representation and fusion of the extracted relevant information across multiple biometric modalities. In this paper, we propose feature level fusion by adopting the concept of canonical correlation analysis (CCA) to fuse Iris and Fingerprint feature sets of the same person. The uniqueness of this approach is that it extracts maximized correlated features from feature sets of both modalities as effective discriminant information within the features sets. CCA is, therefore, suitable to analyze the underlying relationship between two feature spaces and generates more powerful feature vectors by removing redundant information. We demonstrate that an efficient multimodal recognition can be achieved with a significant reduction in feature dimensions with less computational complexity and recognition time less than one second by exploiting CCA based joint feature fusion and optimization. To evaluate the performance of the proposed system, Left and Right Iris, and thumb Fingerprints from both hands of the SDUMLA-HMT multimodal dataset are considered in this experiment. We show that our proposed approach significantly outperforms in terms of equal error rate (EER) than unimodal system recognition performance. We also demonstrate that CCA based feature fusion excels than the match score level fusion. Further, an exploration of the correlation between Right Iris and Left Fingerprint images (EER of 0.1050%), and Left Iris and Right Fingerprint images (EER of 1.4286%) are also presented to consider the effect of feature dominance and laterality of the selected modalities for the robust multimodal biometric system.</p></abstract>

<p>Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segmented to extract the iris region. In the second step, wavelet packet decomposition is conducted to extract features of the iris image, since good time and frequency resolutions can be provided simultaneously by the wavelet packet decomposition. In the next step, all decomposed nodes or packets are arranged as a 3^rd order tensor rather than a long vector, in which feature fusion is directly implemented with multilinear principal component analysis (MPCA). This approach provides a more compact or useful low-dimensional representation directly from the original tensorial representation. Finally, a discriminative tensor feature selection mechanism and classification strategy are applied to iris recognition problem. The obtained results indicate the usefulness of MPCA to select discriminative features and fuse them effectively. The experimental results reveal that the proposed tensor-based MPCA approach achieved a competitive matching performance on the SDUMLA-HMT Iris database with an adequate acceptable rate.</p>

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