Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras

Autor: Barchid, Sami, Allaert, Benjamin, Aissaoui, Amel, Mennesson, José, Djéraba, Chaabane

Facial Expression Recognition (FER) is an active research domain that has shown great progress recently, notably thanks to the use of large deep learning models. However, such approaches are particularly energy intensive, which makes their deployment

Externí odkaz: http://arxiv.org/abs/2304.10211

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Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras: Facial Expression Recognition (FER) is an active research domain that has shown great progress recently, notably thanks to the use of large deep learning models. However, such approaches are particularly energy intensive, which makes their deployment difficult for edge devices. To address this issue, Spiking Neural Networks (SNNs) coupled with event cameras are a promising alternative, capable of processing sparse and asynchronous events with lower energy consumption. In this paper, we establish the first use of event cameras for FER, named 'Event-based FER', and propose the first related benchmarks by converting popular video FER datasets to event streams. To deal with this new task, we propose 'Spiking-FER', a deep convolutional SNN model, and compare it against a similar Artificial Neural Network (ANN). Experiments show that the proposed approach achieves comparable performance to the ANN architecture, while consuming less energy by orders of magnitude (up to 65.39x). In addition, an experimental study of various event-based data augmentation techniques is performed to provide insights into the efficient transformations specific to event-based FER

Autor: Barchid, Sami, Allaert, Benjamin, Aissaoui, Amel, Mennesson, José, Djéraba, Chaabane

Publikováno v: 20th International Conference on Content-based Multimedia Indexing
20th International Conference on Content-based Multimedia Indexing, Sep 2023, Orléans (France), France

International audience; Facial Expression Recognition (FER) is an active research domain that has shown great progress recently, notably thanks to the use of large deep learning models. However, such approaches are particularly energy intensive, whic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::571a9d4454e1dbdea70c660d2f7cc7af
http://arxiv.org/abs/2304.10211

Construction and performance analysis of a new SAC-OCDMA code based on Latin square matrix.

Autor: AISSAOUI, Amel¹ aissaoui.amel@umc.edu.dz, HACINI, Latifa²

Publikováno v: Turkish Journal of Electrical Engineering & Computer Sciences. 2020, Vol. 28 Issue 5, p2630-2642. 13p.

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