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Pixel Club: What's in a Face? Metric Learning for Face Characterization
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Omry Sendik (Tel-Aviv University)
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Tuesday, 3.12.2019, 10:00
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Electrical Eng. Building 1061
We present a method for determining which facial parts (mouth, nose, etc.) best characterize an individual, given a set of that individual's portraits. We introduce a novel distinctiveness analysis of a set of portraits, which leverages the deep features extracted by a pre‐trained face recognition CNN and a hair segmentation FCN, in the context of a weakly supervised metric learning scheme. Our analysis enables the generation of a polarized class activation map (PCAM) for an individual's portrait via a transformation that localizes and amplifies the discriminative regions of the deep feature maps extracted by the aforementioned networks. A user study that we conducted shows that there is a surprisingly good agreement between the face parts that users indicate as characteristic and the face parts automatically selected by our method. We demonstrate a few applications of our method, including determining the most and the least representative portraits among a set of portraits of an individual, and the creation of facial hybrids: portraits that combine the characteristic recognizable facial features of two individuals. Our face characterization analysis is also effective for ranking portraits in order to find an individual's look‐alikes (Doppelgängers).

Omry currently heads the SoC algorithms group (a total of roughly 40 algorithm engineers) in Samsung Israel R&D Center. His focus is on developing ISP, CV and ML algorithms, targeted to the automotive market (mainly ADAS). In addition to that, he's a PhD candidate in the school of Computer Science of Tel-Aviv University, under the joint supervision of Prof. Daniel Cohen-Or and Prof. Dani Lischinski. His main research interest include employing pre-trained neural networks for the purpose of image synthesis.

Omry completed his MSc in the school of Electrical Engineering of Tel-Aviv University, where he was supervised by Prof. Hagit Messer Yaron. His thesis was in the sampling theory realm. Prior to that, he obtained a BSc in Electrical Engineering and a BA in Physics both at the Technion.
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