Work in progress: a quantitative study of effectiveness in group learning

It is generally assumed that group studies are more effective for students than individual studies. The objective of this work in progress is to quantitatively evaluate and analyze the effect of collaborative studies on individual student’s performance. This effort would help the student stimulate interest in group learning and collaboration along with exposing them towards multiple problem solving approaches while working individually or in groups. This way the students are challenged to use their existing knowledge and approach, and augment it further with the knowledge and approach provided by group partners. While there are several efforts that focus on developing new group learning techniques, we intend to study the efficacy of previously proposed techniques under various test settings for EE and CS courses without significantly diverting from the course framework

Optimization methods and Quadratic Programming

Optimization is the process of maximizing or minimizing the objective function which satisfies the given constraints. There are two types of optimization problem linear and nonlinear. Linear optimization problem has wide range of applications, but all realistic problem cannot be modeled as linear program, so here non-linear programming gains its importance. In the present work I have tried to find the solution of non-linear programming Quadratic problem under different conditions such as when constraints are not present and when constraints are present in the form of equality and inequality sign. Graphical method is also highly efficient in solving problems in two dimensions. Wolfe’s modified simplex method helps in solving the Quadratic programming problem by converting the quadratic problem in successive stages to linear programming which can be solved easily by applying two – phase simplex method. A variety of problems arising in the area of engineering, management etc. are modeled as optimization problem thus making optimization an important branch of modern applied mathematics

Mitigating the effect of covariates in face recognition

Current face recognition systems capture faces of cooperative individuals in controlled environment as part of the face recognition process. It is therefore possible to control lighting, pose, background, and quality of images. However, in a real world application, we have to deal with both ideal and imperfect data. Performance of current face recognition systems is affected for such non-ideal and challenging cases. This research focuses on designing algorithms to mitigate the effect of covariates in face recognition.;To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations. Unlike the conventional method, the gallery face image is transformed with respect to the probe face image and facial features are extracted from the registered gallery and probe face images. The variations due to disguises cause change in visual perception, alter actual data, make pertinent facial information disappear, mask features to varying degrees, or introduce extraneous artifacts in the face image. To recognize face images with variations due to age progression and disguises, a granular face verification approach is designed which uses dynamic feed-forward neural architecture to extract 2D log polar Gabor phase features at different granularity levels. The granular levels provide non-disjoint spatial information which is combined using the proposed likelihood ratio based Support Vector Machine match score fusion algorithm. The face verification algorithm is validated using five face databases including the Notre Dame face database, FG-Net face database and three disguise face databases.;The information in visible spectrum images is compromised due to improper illumination whereas infrared images provide invariance to illumination and expression. A multispectral face image fusion algorithm is proposed to address the variations in illumination. The Support Vector Machine based image fusion algorithm learns the properties of the multispectral face images at different resolution and granularity levels to determine optimal information and combines them to generate a fused image. Experiments on the Equinox and Notre Dame multispectral face databases show that the proposed algorithm outperforms existing algorithms. We next propose a face mosaicing algorithm to address the challenge due to pose variations. The mosaicing algorithm generates a composite face image during enrollment using the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a users face image. Experiments conducted on three different databases indicate that face mosaicing offers significant benefits by accounting for the pose variations that are commonly observed in face images.;Finally, the concept of online learning is introduced to address the problem of classifier re-training and update. A learning scheme for Support Vector Machine is designed to train the classifier in online mode. This enables the classifier to update the decision hyperplane in order to account for the newly enrolled subjects. On a heterogeneous near infrared face database, the case study using Principal Component Analysis and C2 feature algorithms shows that the proposed online classifier significantly improves the verification performance both in terms of accuracy and computational time

Unconstrained face recognition for law enforcement applications

This research work simulates the human visual cortex by using 2D log polar Gabor wavelet to extract the facial features. Scale and orientation independent convolution of face image with Gabor wavelet gives the features in the form of amplitude and phase. The proposed face recognition algorithm is invariant to frequency, scale, filter orientation, illumination, and contrast. We evaluated the recognition algorithm on four face databases namely FERET, CMU AMP, CMU PIE and Notre Dame Face databases. Experimental results show that using single image for training, phase feature based face recognition performs approximately 5% better than amplitude feature based face recognition.;Another facet of this research involves matching scanned and digital face images. Normalization and transformation algorithms are proposed to resample the scanned and the digital images into one common domain. Validation is performed on a face database of 500 classes containing both the scanned and digital face images.;Finally, a synthetic face database is prepared to evaluate the performance of the proposed face recognition algorithm with disguise. The database includes synthetic face images with single and multiple variations in appearance and feature. Results show that the proposed algorithm outperforms other recognition algorithms