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                            Library & Multimedia
 NIJ Face Algorithm Assessment - Phases I, II, and III, Version 1.0
NCJ Number
              252825

Date Published

March 2019
Length
              142 pages
Annotation
              Findings and methodology are presented for an assessment of the facial image processing software developed by Carnegie Mellon University (CMU).
Abstract
              The assessment was divided into three phases, one for each of the three algorithms (Face Detection, Face Recognition, and Periocular Face Reconstruction). Results were computed with a software framework to maximize the consistency of the assessments. The process was designed to provide a one-to-one comparison between algorithms by processing both the benchmarks and the algorithm being assessed, using the same dataset and metrics. Summary results for the assessment are provided for each phase. In each case, the metric values are broken out by algorithm and dataset. The CMU-generated, CNN-based software generally out-performed the benchmark comparisons, although there were a few exceptions that are noted. The assessment found the CMU Ultron performance to be comparable to tinyFace. PittPatt performance is inferior to the Ultron/tinyFace, and YOLO has the lowest performance. In the recognition domain, the CMU-developed Native CMU outperformed across three datasets without any tuning. 28 references and extensive tables and figures
      
  
          Additional Details
Grant Number(s)
        2013-MU-CX-K111


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Establishing a National Criminal Justice Technology Research, Test and Evaluation Center
    
  
  
Date Published: March 1, 2019
    
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