Portrait of Eyal Krupka

Eyal Krupka

Partner Research Manager


Dr. Eyal Krupka leads computer vision and machine learning research in Advanced Technology Labs. He has joined Microsoft about Eight years ago. His recent research work has focused on hand pose and gesture recognition. Previous work includes face recognition for in Xbox One.

Prior to Microsoft, Eyal worked for Intel Research and was a staff researcher for DSPC. Eyal has 24 years of experience leading R&D projects in a wide range of fields: machine learning, computer vision, signal and image processing, digital communications, software engineering and hardware design. He is the inventor of more than 25 patents and has been published in top machine learning conferences/journals. Eyal received his Ph.D. in Computational Neuroscience from the Hebrew University in Jerusalem, and his B.Sc. (Summa Cum Laude) in Electrical Engineering from the Technion, Israel’s Institute of Technology.


Fully Articulated Hand Tracking

Established: October 2, 2014

We present a new real-time articulated hand tracker which can enable new possibilities for human-computer interaction (HCI). Our system accurately reconstructs complex hand poses across a variety of subjects using only a single depth camera. It also allows for a high-degree of robustness, continually recovering from tracking failures. However, the most unique aspect of our tracker is its flexibility in terms of camera placement and operating range.   Screenshots Please note, we…

Sparse Reflections Analysis

Established: June 30, 2014

We present Sparse Reflections Analysis (SRA), an algorithm for removing multipath interference from Time of Flight sensors. SRA allows for very general forms of multipath, including interference with three or more paths, diffuse multipath resulting from Lambertian surfaces, and combinations thereof. SRA removes this general multipath with robust techniques based on L1 optimization. Further, due to a novel dimension reduction, we present a very fast version of SRA, which can run at frame rate.

Microsoft 3-Handpose dataset

Established: April 13, 2014

We publish a subset of the data from the paper "Discriminative Ferns Ensemble for Hand Pose Recognition". To receive a download link for the dataset please send your request to ThreeHandPose@microsoft.com. Data description: The data comprises 80,000 hand pose images of several subjects collected by the Xbox One team. Each image consists of three (3) channels each of size 36 by 36 pixels: binary mask, masked IR, masked depth. Every image shows a hand in…