Computer Science Student @ The University of Texas at Austin
Hello! I am currently a 4th year undergraduate student at The University of Texas at Austin, studying computer science as a part of the Turing Scholars Program. I am also pursuing a Masters degree in Computer Science via the 5-Year BS/MS Integrated Program with an expected graduation date of May 2019. At UT, I am a member of the Graphics & AI research lab, directed by Dr. Qixing Huang. My research interests are primarily in 2D/3D computer vision, with projects related to viewpoint/keypoint estimation and domain adaptation.
Unsupervised Domain Adaptation for 3D Keypoint Prediction from a Single Depth Scan
Xingyi Zhou, Arjun Karpur, Chuang Gan, Linjie Lou, Qixing Huang
CVPR2018 Submission - [PDF] [Code] [Data]
We introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan/image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency provides effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general purpose domain adaptation techniques.
Multiple User Biometric for Authentication to Secured Resources
Jim Baca, Arjun Karpur, Dhaval Patel, Preetham Shambhat, Naissa Conde, Prital Shah, A.G. Ramesh, Tobias Kohlenberg
US Patent 9,646,216 - [PDF]
Various embodiments are generally directed to the provision and use of multiple person biometric authentication systems. An apparatus including a processor element and logic executable by the processor component is disclosed. The logic is configured to cause the apparatus to receive information including an indication of a plurality of biometric measurements and generate a combined biometric indicator based in part on the plurality of biometric measurements. The combined biometric indicator can be generated using fuzzy hashing.