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 Exploring suitable classifier for robust face and facial expression recognition.(Dhirubhai Ambani Institute of Information and Communication Technology, 2013) Jain, Deshna; Mitra, Suman K.Face recognition by machines has been studied since last few decades and the problem is attempted to be solved by various ways. However any robust solution is not acheived yet by the researchers due to numerous challenges involved like illumination changes, pose variation, occlusion, cluttered background, noncooperation of the subject and ageing effect on human face. We have worked by modelling the problem as a pattern recogniton problem. The solution of this problem involves mainly three steps: (a) Face detection and segmentation, (b) Feature extraction, and (c) Classification or recognition. We have worked on finding the robust classifier for face and facial expression recognition. Naive Bayes Classifier (NBC) is the statistical classifier that works by estimating the maximum probability of the possible classes to which the testing data point may belong assuming that the features are mutually independent. It makes use of Bayes rule for likelihood computation. This approach works well if the distribution of the features is known accurately. Otherwise, probability distribution of the features belonging to corresponding classes has to be estimated with density estimation techniques. Here features are assumed to follow Gaussian distribution. Experiments are done for classifying faces from YALE face database and DAIICT database, taking ELPP coefficients as the features. Another classifier we used is Support Vector Machine (SVM) that works by finding the decision plane between two classes. It finds the decision plane with the help of support vectors having maximum margin between them. Experiments performed with SVM give better results than NBC for both DAIICT and YALE face database. While using NBC, one of the estimation techniques that is used in this work is Kernel Density Estimation also known as Parzen window. The approach estimates the density of a point for a given dataset with a global bandwidth. This classification technique is used for face recognition using YALE face database and DAIICT database. For DAIICT database the estimation method shows different results for the same dataset with different parameters whereas no significant results are obtained for YALE face database. On the other hand, in the whole algorithm there is no measure of best fit of the estimatd curve involved. These issues are resolved by using Pearson’s chi-squared test for testing goodness of fit of the estimation with changing parameters of the selected bandwidth. In addition to this, bandwidth is kept dynamic by computing it with neighboring datapoints instead of keeping it global. This approach performs better than the former one for YALE face database and equivalent for DAIICT database. The experiments are extended for classifying the facial expressions as well. A comparision of KNN, NBC, proposed approach for NBC and SVM is presented in the work. SVM outperformed all the classifiers for both the databases.Item Open Access Study of face recognition systems(Dhirubhai Ambani Institute of Information and Communication Technology, 2006) Patel, Hima M.; Mitra, Suman K.Face Recognition comes under the general area of object recognition and has attracted researchers in the pattern recognition community for the past thirty years. The significance of this area has grown rapidly largely for surveillance purposes. This thesis is on a study of face recognition techniques. Three new algorithms have been proposed, implemented and tested using standard databases and encouraging results have been obtained for all of them. The first algorithm uses modular Principal Component Analysis (PCA) for feature extraction and a multi class SVM classifier for classification. The algorithm has been tested for frontal face images, face images with variations in expression, pose and illumination conditions. Experimental results denote a 100% classification accuracy on frontal faces, 95% accuracy on expression variation images, 78% for pose variation and 67% for illumination variation images. The next algorithm concentrates solely on the illumination variation problem. Edginess method based on one dimensional processing of signals is used to extract an edginess map. Application of PCA on the edginess images gives the weight vectors which are used as features to a multi class SVM classifier. An accuracy of 100% has been obtained, proving the method to be tolerant to illumination variations. The final part of the thesis proposes a bayesian framework for face recognition. The nodes of the bayesian classifier are modelled as a Gaussian Mixture Model (GMM) and the parameters of the nodes are learnt using Maximum Likelihood Estimation (MLE) algorithm. The inferencing is done using the junction tree inferencing algorithm. An accuracy of 93.75% has been achieved.Item Open Access Fractal based approach for face recognition(Dhirubhai Ambani Institute of Information and Communication Technology, 2004) Athale, Suprita; Mitra, Suman K.An automated face recognition system is proposed in this dissertation. The system efficiently recognizes a candidate (test) image using the interdependence of the pixel that arises from the fractal compression of the image. The interdependence of the pixels is inherent within the fractal code in the form of chain of pixels. The mechanism of capturing these chains from the fractal codes is called pixel chaining. The present face recognition system tries to match pixel chains of the candidate image with that of the images present in the database. The work domain of the system is fractal codes but not the images. This leads to an advantage towards handling large database of face images.The system performance is found to be very satisfactory with the recognition rate of 98.4%. A minor improvement in the performance of the system over a few existing methods has been observed.