M Tech Dissertations
Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3
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Item Open Access Sparse representation and fisher discriminant criterion based dimensionality reduction for face recognition(2020) Chavda, Parita; Mitra, Suman K.Dimensionality Reduction(DR) is a very popular topic in the field of pattern recognition. Generally, Practical applications like face recognition, object classification, and text categorization include high dimensional data. However, Past research shows that high dimensional image may reside in a low dimensional manifold. Therefore, To understand high dimensional data efficiently dimensionality reduction is a necessary pre-processing step. Many linear, non-linear, neighborhood and kernel-based DR techniques are developed and demonstrated good results in face recognition. All these methods are less efficient in case of a large variation in facial expression, illumination, and pose in realtime face recognition. A few years back, a sparse representation(SR) based classifier(SRC) shown amazing results in classification. To get SR, more number of training samples required than the input image size. In face recognition, training data size is mostly less compare to input image size. So, Dimensionality reduction becomes compulsory in this case before applying SRC. Recently, sparsity-based DR methods such as SPP, SRC-DP, and SRC-FDC are developed and shown great results in real-world face recognition. SPP and SRCDP use sparse reconstruction residual which is not much useful in classification. To overcome this, SRC-FDC uses the Fisher discriminant criterion for better class separation, but it uses random initialization for the initial projection matrix P0. A new DR technique with proper initialization for initial matrix P0 called Initialized SRC-FDC is proposed.Experiments performed on Extended Yale B, CMUPIE, and Coil-20 dataset shows that Initialized SRC-FDC is more effective and efficient than the original SRC-FDC.Item Open Access Locality preserving projection: a study and applications(Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Shikkenawis, Gitam; Mitra, Suman KLocality Preserving Projection (LPP) is a recently proposed approach for dimensionality reduction that preserves the neighbourhood information and obtains a subspace that best detects the essential data manifold structure. Currently it is widely used for finding the intrinsic dimensionality of the data which is usually of high dimension. This characteristic of LPP has made it popular among other available dimensionality reduction approaches such as Principal Component Analysis (PCA). A study on LPP reveals that it tries to preserve the information about nearest neighbours of data points, thus may lead to misclassification in the overlapping regions of two or more classes while performing data analysis. It has also been observed that the dimension reducibility capacity of conventional LPP is much less than that of PCA. A new proposal called Extended LPP (ELPP) which amicably resolves two issues mentioned above is introduced. In particular, a new weighing scheme is designed that pays importance to the data points which are at a moderate distance, in addition to the nearest points. This helps to resolve the ambiguity occurring at the overlapping regions as well as increase the reducibility capacity. LPP is used for a variety of applications for reducing the dimensions one of which is Face Recognition. Face Recognition is one of the most widely used biometric technology for person identification. Face images are represented as highdimensional pixel arrays and due to high correlation between the neighbouring pixel values; they often belong to an intrinsically low dimensional manifold. The distribution of data in a high dimensional space is non-uniform and is generally concentrated around some kind of low dimensional structures. Hence, one of the ways of performing Face Recognition is by reducing the dimensionality of the data and finding the subspace of the manifold in which face images reside. Both LPP and ELPP are used for Face and Expression Recognition tasks. As the aim is to separate the clusters in the embedded space, class membership information may add more discriminating power. With this in mind, the proposal is further extended to the supervised version of LPP (SLPP) that uses the known class labels of data points to enhance the discriminating power along with inheriting the properties of ELPP