Gaurav Goswami

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Mayank Vatsa31
Richa Singh31
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7Afzel Noore
7Brian M. Powell
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On RGB-D face recognition using Kinect
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)
MDLFace: Memorability augmented deep learning for video face recognition
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)
RGB-D Face Recognition With Texture and Attribute Features
Face recognition algorithms generally utilize 2D images for feature extraction and matching. To achieve higher resilience toward covariates, such as expression, illumination, and pose, 3D face recognition algorithms are developed. While it is challenging to use specialized 3D sensors due to high cost, RGB-D images can be captured by low-cost sensors such as… (More)
Face recognition CAPTCHA
CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users… (More)
FR-CAPTCHA: CAPTCHA Based on Recognizing Human Faces
A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is designed to distinguish humans from machines. Most of the existing tests require reading distorted text embedded in a background image. However, many existing CAPTCHAs are either too difficult for humans due to excessive distortions or are trivial for automated… (More)
Recognizing composite sketches with digital face images via SSD dictionary
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)
Harnessing social context for improved face recognition
Face recognition is traditionally based on features extracted from face images which capture various intrinsic characteristics of faces to distinguish between individuals. However, humans do not perform face recognition in isolation and instead utilize a wide variety of contextual cues as well in order to perform accurate recognition. Social context or… (More)