Gaurav Goswami

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Face recognition algorithms generally use 2D images for feature extraction and matching. In order to achieve better performance, 3D faces captured via specialized acquisition methods have been used to develop improved algorithms. While such 3D images remain difficult to obtain due to several issues such as cost and accessibility, RGB-D images captured by(More)
Videos have ample amount of information in the form of frames that can be utilized for feature extraction and matching. However, face images in not all of the frames are " memorable " and useful. Therefore, utilizing all the frames available in a video for recognition does not necessarily improve the performance but significantly increases the computation(More)
—Face recognition algorithms generally utilize 2D images for feature extraction and matching. To achieve higher resilience towards covariates such as expression, illumination and pose, 3D face recognition algorithms are developed. While it is highly challenging to use specialized 3D sensors due to high cost, RGB-D images can be captured by low cost sensors(More)
The increasing use of smartphones, tablets, and other mobile devices poses a significant challenge in providing effective online security. CAPTCHAs, tests for distinguishing human and computer users, have traditionally been popular; however, they face particular difficulties in a modern mobile environment because most of them rely on keyboard input and have(More)
Sketch recognition has important law enforcement applications in detecting and apprehending suspects. Compared to hand drawn sketches, software generated composite sketches are faster to create and require lesser skill sets as well as bring consistency in sketch generation. While sketch generation is one side of the problem, recognizing composite sketches(More)