Face recognition has found several applications ranging from cross border security, surveillance, access control, multimedia to forensics. Face recognition under variations due to pose, illumination, and expression has ...

One of the challenging applications in face recognition is video surveillance, where unconstrained low-resolution video data is captured both in day and night time (visible and near-infrared) with multiple subjects in ...

A low-cap power budget is challenging for exascale computing. Dynamic Voltage and Frequency Scaling (DVFS) and Uncore Frequency Scaling (UFS) are the two widely used techniques for limiting HPC application's energy footprint. ...

Widely adapted locally interpretable methods such as LIME [20] and SHAP [12] fail to capture the underlying causal relationships between the variables. They merely capture the linear and non-linear associations. These ...

With the increase in demand for enterprise wi- , there is a growing need to understand the functioning of these networks. Large scale Wi-Fi deployment is a challenging task. There is a need to diagnose network related ...

Industrial robots are complex systems that require technical expertise for condition monitoring and diagnostics. In small and medium scale industries, adequate skilled resources may not be available to monitor the robots ...

Education is now often regarded as the most important contributor to a country’s economic prosperity and stability. Digitalization plays a key part in the development and progress of every area in today’s world. The education ...

With the increased interest in face recognition across di erent applications, the research in this area has ourished over the past few decades. However, face recognition with disguise variations has gained little ...

First person videos captured from wearable cameras are growing in popularity.Standard algo- rithms developed for third person videos often do not work for such egocentric videos because of drastic change in camera ...

We use Mean Shift clustering in the latent space of an auto-encoder to have a better representation of the data and a more structured latent space. Instead of just using the mode of the distribution calculated using kernel ...