Models, code, and papers for "Rahul Bhatia":
Augmenting human computer interaction with automated analysis and synthesis of facial expressions is a goal towards which much research effort has been devoted recently. Facial gesture recognition is one of the important component of natural human-machine interfaces; it may also be used in behavioural science, security systems and in clinical practice. Although humans recognise facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenge. The face expression recognition problem is challenging because different individuals display the same expression differently. This paper presents an overview of gesture recognition in real time using the concepts of correlation and Mahalanobis distance.We consider the six universal emotional categories namely joy, anger, fear, disgust, sadness and surprise.
Finding appropriate inductive loop invariants for a program is a key challenge in verifying its functional properties. Although the problem is undecidable in general, several heuristics have been proposed to handle practical programs that tend to have simple control-flow structures. However, these heuristics only work well when the space of invariants is small. On the other hand, machine-learned techniques that use continuous optimization have a high sample complexity, i.e., the number of invariant guesses and the associated counterexamples, since the invariant is required to exactly satisfy a specification. We propose a novel technique that is able to solve complex verification problems involving programs with larger number of variables and non-linear specifications. We formulate an invariant as a piecewise low-degree polynomial, and reduce the problem of synthesizing it to a set of integer linear programming (ILP) problems. This enables the use of state-of-the-art ILP techniques that combine enumerative search with continuous optimization; thus ensuring fast convergence for a large class of verification tasks while still ensuring low sample complexity. We instantiate our technique as the open-source oasis tool using an off-the-shelf ILP solver, and evaluate it on more than 300 benchmark tasks collected from the annual SyGuS competition and recent prior work. Our experiments show that oasis outperforms the state-of-the-art tools, including the winner of last year's SyGuS competition, and is able to solve 9 challenging tasks that existing tools fail on.