In the IACV lab, we are looking at different problems in computer vision. Now-a- days with increasing security concerns, surveillance cameras are installed everywhere, from shopping malls, airports, and even in personal homes. One of the main objectives of surveillance is to recognize the facial images captured by these cameras.
This is very challenging, since the images usually have quite poor resolution, in addition to uncontrolled illumination, pose and expression. We are developing novel algorithms for matching the low-quality facial images captured using surveillance cameras. This is also extended to general objects as well. We are also working on traffic surveillance, especially for Indian scenarios, like vehicle detection, classification and license plate recognition, etc. Another major area we are looking at is Cross-Modal Matching. Due to increase in the number of sources of data, research in cross-modal matching is becoming an increasingly important area of research. It has several applications like matching text with image, matching near infra-red images with visible images (eg, for matching face images captured during night-time or low-light conditions to standard visible light images in the database), matching sketch images with pictures for forensic applications, etc. We are developing novel algorithms for this problem, which is extremely challenging, due to significant differences between data from different modalities.