Hariprasad Kannan

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We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are(More)
As electronic gadgets become more user friendly, we find natural interaction with the gadgets becoming increasingly popular. Haptic devices have become immensely popular and touch and gesture based interfaces are the logical extension to natural user interaction. We propose to use facial expressions for natural interaction, especially for gadgets like smart(More)
This paper presents a strategy for a nonholonomic mobile robot to autonomously follow a target based on vision information from an onboard pan camera unit (PCU). Homography-based techniques are used to obtain relative position and orientation information from the monocular camera images. The proposed kinematic controller, based on the Lyapunov method,(More)
We present an algorithm to estimate the pose of a human head from a single image. It builds on the fact that only a limited set of cues are required to estimate human head pose and that most images contain far too many details than what are required for this task. Thus, non-photorealistic rendering is first used to eliminate irrelevant details from the(More)
Linear programming relaxations are central to MAP inference in discrete Markov Random Fields. The ability to properly solve the Lagrangian dual is a critical component of such methods. In this paper, we study the benefit of using Newton-type methods to solve the Lagrangian dual of a smooth version of the problem. We investigate their ability to achieve(More)
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