TensorFlow for Generating Edge Detection Dataset

Alpatov, Boris A.; Shubin, Nikita; Yakovlev, Andrey V.

doi:10.1109/meco49872.2020.9134188

Cited by 2 publications

(1 citation statement)

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“…Note that, regardless of the classification approach, the preprocessing and feature extraction might be the most computationally intensive part of the inference stage. Moreover, in the last years, the development of open-source deep learning frameworks for constrained devices, such as TensorFlow Lite, are leading to the use of neural-based inference approaches [ 39 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Fear Recognition for Women Using a Reduced Set of Physiological Signals

Miranda

Canabal

Gutiérrez-Martín

et al. 2021

Sensors

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Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.

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Section: Background and Related Workmentioning

confidence: 99%