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

Deisy Chaves^1,2, Eduardo Fidalgo^1,2, Enrique Alegre^1,2

¹Department of Electrical, Systems and Automation, Universidad de León, 24007 León, Spain.

Sensors (Basel, Switzerland)

|August 16, 2020

Summary

This summary is machine-generated.

Optimizing deep learning face detection for forensics requires balancing speed and accuracy. Resizing images significantly impacts performance on CPUs and GPUs, with a regression model accurately predicting outcomes.

Keywords:

Benchmark CPU CSEM GPU deep learning face detection regression

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Deep Neural Networks for Image-Based Dietary Assessment

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Area of Science:

Computer Vision
Forensic Science
Artificial Intelligence

Background:

Face recognition is crucial for criminal investigations, aiding in identifying fugitives and in cases of child sexual abuse.
Deep learning face detectors offer high accuracy but demand substantial computational resources and processing time.
Real-world forensic applications necessitate face recognition systems that handle low-quality images and meet real-time processing demands.

Purpose of the Study:

To evaluate the speed-accuracy trade-off of popular deep learning face detectors on diverse datasets and hardware.
To develop a predictive model for estimating the performance of face detection systems.
To provide a practical tool for forensic laboratories to optimize face detection strategies.

Main Methods:

Assessed three deep learning face detectors on WIDER Face and UFDD datasets across various CPUs and GPUs.
Investigated the impact of image resizing on detection speed and accuracy.
Developed a multiple linear regression model to predict performance metrics (speed and accuracy).

Main Results:

The optimal speed-accuracy trade-off was achieved by resizing images to 50% on GPUs and 25% on CPUs.
The developed regression model demonstrated a Mean Absolute Error (MAE) of 0.113 in performance estimation.
This predictive capability is highly promising for practical forensic applications.

Conclusions:

Image resizing is a critical factor in optimizing deep learning face detection for forensic use.
A reliable regression model can accurately estimate face detection performance, aiding forensic practitioners.
The findings offer valuable insights for deploying efficient and effective face recognition tools in forensic laboratories.