Python implementation of the SwiftFace face detection algorithm
@inproceedings{10.1145/3471391.3471418,
author = {Ramos, Leonardo and Bautista, Susana and Bonett, Marlon C\'{a}rdenas},
title = {SwiftFace: Real-Time Face Detection: SwitFace},
year = {2021},
isbn = {9781450375979},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3471391.3471418},
doi = {10.1145/3471391.3471418},
abstract = {Computer vision is a field of artificial intelligence that trains computers to interpret the visual world in a way similar to that of humans. Due to the rapid advancements in technology and the increasing availability of sufficiently large training datasets, the topics within computer vision have experienced a steep growth in the last decade. Among them, one of the most promising fields is face detection. Being used daily in a wide variety of fields; from mobile apps and augmented reality for entertainment purposes, to social studies and security cameras; designing high-performance models for face detection is crucial. On top of that, with the aforementioned growth in face detection technologies, precision and accuracy are no longer the only relevant factors: for real-time face detection, speed of detection is essential. SwiftFace is a novel deep learning model created solely to be a fast face detection model. By focusing only on detecting faces, SwiftFace performs 30\% faster than current state-of-the-art face detection models.},
booktitle = {Proceedings of the XXI International Conference on Human Computer Interaction},
articleno = {24},
numpages = {5},
keywords = {neural networks, Face detection, unsupervised deep learning, object detection},
location = {M\'{a}laga, Spain},
series = {Interacci\'{o}n '21}
}