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Assessment and estimation of face detection performance based on deep learning for forensic applications

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Τίτλος: Assessment and estimation of face detection performance based on deep learning for forensic applications
Συγγραφείς: Chaves, Deisy, Fidalgo, Eduardo, Alegre, Enrique, Alaiz-Rodríguez, Rocío, Jáñez-Martino, Francisco, Azzopardi, George
Στοιχεία εκδότη: MDPI AG
Έτος έκδοσης: 2020
Συλλογή: University of Malta: OAR@UM / L-Università ta' Malta
Θεματικοί όροι: Human face recognition (Computer science) -- Technological innovations, Deep learning (Machine learning), Crime laboratories -- Equipment and supplies, Regression analysis -- Computer programs, Image processing -- Digital techniques
Περιγραφή: 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 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, which is very promising for the forensic field. ; peer-reviewed
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://www.um.edu.mt/library/oar/handle/123456789/132589
DOI: 10.3390/s20164491
Διαθεσιμότητα: https://www.um.edu.mt/library/oar/handle/123456789/132589
https://doi.org/10.3390/s20164491
Rights: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
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  – Url: https://www.um.edu.mt/library/oar/handle/123456789/132589#
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  Data: Assessment and estimation of face detection performance based on deep learning for forensic applications
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  Data: <searchLink fieldCode="AR" term="%22Chaves%2C+Deisy%22">Chaves, Deisy</searchLink><br /><searchLink fieldCode="AR" term="%22Fidalgo%2C+Eduardo%22">Fidalgo, Eduardo</searchLink><br /><searchLink fieldCode="AR" term="%22Alegre%2C+Enrique%22">Alegre, Enrique</searchLink><br /><searchLink fieldCode="AR" term="%22Alaiz-Rodríguez%2C+Rocío%22">Alaiz-Rodríguez, Rocío</searchLink><br /><searchLink fieldCode="AR" term="%22Jáñez-Martino%2C+Francisco%22">Jáñez-Martino, Francisco</searchLink><br /><searchLink fieldCode="AR" term="%22Azzopardi%2C+George%22">Azzopardi, George</searchLink>
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  Data: MDPI AG
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  Data: 2020
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  Data: University of Malta: OAR@UM / L-Università ta' Malta
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  Data: <searchLink fieldCode="DE" term="%22Human+face+recognition+%28Computer+science%29+--+Technological+innovations%22">Human face recognition (Computer science) -- Technological innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning+%28Machine+learning%29%22">Deep learning (Machine learning)</searchLink><br /><searchLink fieldCode="DE" term="%22Crime+laboratories+--+Equipment+and+supplies%22">Crime laboratories -- Equipment and supplies</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis+--+Computer+programs%22">Regression analysis -- Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing+--+Digital+techniques%22">Image processing -- Digital techniques</searchLink>
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  Data: 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 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, which is very promising for the forensic field. ; peer-reviewed
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  Data: 10.3390/s20164491
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  Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
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      – SubjectFull: Human face recognition (Computer science) -- Technological innovations
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      – SubjectFull: Deep learning (Machine learning)
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      – SubjectFull: Crime laboratories -- Equipment and supplies
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      – TitleFull: Assessment and estimation of face detection performance based on deep learning for forensic applications
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