Algorithms that Mimic the Brain's Processing Networks Show Promise For Producing Superior Face Detection and Recognition Technology

• NCJ Number: 254567
• Date Published: March 2020
• Length: 5 pages
• Grant Number(s): 2013-MU-CX-K111
• Agencies: NIJ
• Publication Type: Research (Applied/Empirical)
• Award: Establishing a National Criminal Justice Technology Research, Test and Evaluation Center

Annotation

This article details NIJ's recently funded research that compares deep neural network algorithms designed for face detection or recognition with other, older products. 

Abstract

The lack of apples-to-apples evaluations of face-detection and face-recognition algorithms has left some members of law enforcement and the security industry uncertain of what will work best for certain purposes. To help remedy this issue, the National Institute of Justice recently funded research that compared deep neural network algorithms designed for face detection or recognition with other, older products. The latter included PittPatt, the conventional algorithm long widely used by law enforcement for detection and recognition functions. Evaluations were performed by the National Criminal Justice Technology Research, Test, and Evaluation Center at the Johns Hopkins University Applied Physics Laboratory. This article discusses the test results that indicated high-powered, deep neural networks can deliver improved performance.