Publications

@article{chaves_assessment_2020,

title = {Assessment and estimation of face detection performance based on deep learning for forensic applications},

author = {Deisy Chaves and Eduardo Fidalgo and Enrique Alegre and Rocío Alaiz-Rodríguez and Francisco Jáñez-Martino and George Azzopardi},

url = {https://www.mdpi.com/1424-8220/20/16/4491},

year  = {2020},

date = {2020-01-01},

journal = {Sensors},

volume = {20},

number = {16},

pages = {4491},

abstract = {This paper explores the effectiveness of deep learning-based face recognition as a forensic tool for criminal investigations. The authors evaluate the speed–accuracy tradeoff of three popular face detectors on the WIDER Face and UFDD datasets using different CPUs and GPUs. They develop a regression model to estimate performance in terms of processing time and accuracy, which could assist forensic laboratories in selecting optimal detection options. Experimental results suggest that the best tradeoff is achieved with 50% image resizing on GPUs and 25% on CPUs, while their regression model achieves a Mean Absolute Error (MAE) of 0.113, demonstrating its potential for forensic applications.},

note = {Publisher: MDPI},

keywords = {deep learning, face recognition, forensic science, speed-accuracy tradeoff},

pubstate = {published},

tppubtype = {article}

}