Chaves, D.; Fidalgo, E.; Alegre, E.; Alaiz-Rodríguez, R.; Jáñez-Martino, F.; Azzopardi, G. Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications. Sensors2020, 20, 4491.
Chaves, D.; Fidalgo, E.; Alegre, E.; Alaiz-Rodríguez, R.; Jáñez-Martino, F.; Azzopardi, G. Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications. Sensors 2020, 20, 4491.
Chaves, D.; Fidalgo, E.; Alegre, E.; Alaiz-Rodríguez, R.; Jáñez-Martino, F.; Azzopardi, G. Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications. Sensors2020, 20, 4491.
Chaves, D.; Fidalgo, E.; Alegre, E.; Alaiz-Rodríguez, R.; Jáñez-Martino, F.; Azzopardi, G. Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications. Sensors 2020, 20, 4491.
Abstract
Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require a large computation power and processing time. In this work, we evaluate the speed-accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed-accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113 what is very promising for the forensic field.
Keywords
Face detection; CSEM; Deep learning; GPU; CPU; Benchmark; Regression
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.