Working Paper Article Version 1 This version is not peer-reviewed

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Version 1 : Received: 24 July 2020 / Approved: 27 July 2020 / Online: 27 July 2020 (14:54:15 CEST)

A peer-reviewed article of this Preprint also exists.

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. Sensors 2020, 20, 4491.

Journal reference: Sensors 2020, 20, 4491
DOI: 10.3390/s20164491

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.

Subject Areas

Face detection; CSEM; Deep learning; GPU; CPU; Benchmark; Regression

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