Distillation of an End-to-End Oracle for Face Verification and Recognition Sensors.

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ID: 103837
2020
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Abstract
Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.
Reference Key
guzzi2020distillationsensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Guzzi, Francesco;De Bortoli, Luca;Molina, Romina Soledad;Marsi, Stefano;Carrato, Sergio;Ramponi, Giovanni;
Journal sensors
Year 2020
DOI
E1369
URL
Keywords
Deep learning convolutional neural networks distillation face recognition biometric sensors face verification spatial transformer network

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