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This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
Face recognition systems have been playing a vital role from several decades. Thus, various algorithms for face recognition are developed for various applications like ‘person identification’, ‘human computer interaction’, ‘security systems’. A framework for face recognition with different poses through face reconstruction is being proposed in this paper. In the present work, the system is trained with only a single frontal face with normal illumination and expression. Instead of capturing the image of a person in different poses using camera or video, different views of the 3D face are reconstructed with the help of a 3D face shape model. This automatically increases the size of the training set. This approach outperforms the present 2D techniques with higher recognition rate. This paper refers to the face detection and recognition approach, which primarily focuses on Enhanced Independent Component Analysis(EICA) for the Query Based Face Retrieval and the implementation is done in Scilab. This method detects the static face(cropped photo as input) and also faces from group picture, and these faces are reconstructed using 3D face shape model. Image preprocessing is used inorder to reduce the error rate when there are illuminated images. Scilab’s SIVP toolbox is used for image analysis.
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