Promote learning and system development in the field of Computer Vision and Biometrics. The research community will be constantly working to exchange ideas and become accustomed to the field through formal interactions with the experts of the domain. The lab will work as a channel to bring project funding from Government and Private sectors, which will facilitate member students, faculty and researchers to work on the real world problems in a collaborative environment.
Biometrics for Humanity, Cognitive Biometrics, Multimodel Biometrics, Machine Learning for Biometrics, Python and Deep Learning Libraries (Tensorflow), Face Recognition using Deep Neural Network, Image Captioning using Deep Learning, Multimodel biometric recognition using Deep Architecture
Graphical models provide a powerful framework for machine learning that allows effective representation of dependency between disparate factors and enables the use of principled probabilistic methods for inference and parameter estimation. This course provides an introduction to graphical models. The course will also feature a set of hands on computational assignments that will help students develop deeper familiarity with the models.
Publishing a research paper is usually an exciting experience for most researchers. In this excitement, it is important to not forget that the writing process for the first few manuscripts also often lays the ground for future habits. This presentation, intended for authors unfamiliar with the process of publishing a technical paper, provides a guide to established etiquette and ethics in scholarly publishing.