Two-tiered face verification with low-memory footprint for mobile devices
Padilha, Rafael; Andaló, Fernanda; Bertocco, Gabriel; Almeida, Waldir; Dias, William; Resek, Thiago; Torres, Ricardo Da Silva; Wainer, Jacques; Rocha, Anderson
Peer reviewed, Journal article
Published version
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Date
2020Metadata
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- Institutt for IKT og realfag [597]
- Publikasjoner fra CRIStin - NTNU [38289]
Abstract
Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication. In this work, the authors propose a facial verification method optimised to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features and a convolutional neural network (CNN) to verify if the person depicted in a photograph corresponds to the device owner. To train a CNN for the verification task, the authors propose a hybrid-image input, which allows the network to process encoded information of a pair of face images. The proposed experiments show that the solution outperforms state of the art face verification methods, providing a 4× speedup when processing an image in recent smartphone models. Additionally, the authors show that the two-tiered procedure can be coupled with existing face verification CNNs improving their accuracy and efficiency. They also present a new data set of selfie pictures – RECOD Selfie data set – that hopefully will support future research in this scenario.