U.S. Government Accountability Office Urges Facial Recognition Regulation

By Zach Segal, Published Aug 27, 2020, 10:22am EDT (Info+)

The US Government Accountability Office (GAO) is urging facial recognition regulation in a 65-page report. IPVM has reviewed this report, offers analysis on it, and the 3 main steps we recommend they take to achieve this.

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Executive *******

****.*. ********** ************** ****** (***)****** ********** **** *********** ****** ** open *** ***********, ******* ** ******** uses, ********** ** **** ******* ********* collected ****, ***** **** **** *** most ******** *** ***** ****** **********, and ******** *** **** *** **** protection. **** ****** ********* ******'*************** **** ******** ********** ******** ********** laws ** ******* *** **********. **** first **** **** ************** * ***** ago ** ** ****** *** **** recognition *** ******* **** **** ***** fire ********, ** *********** ********** ** more ******.

What ** ***

****.*. ********** ************** ******** * *********** **** ** *** U.S. ******** **** ***** ******** ******* issues. *** ** ******** ** ** the************* ********. **** *** ***** ****** **** ensuring ******** ********* *** ***** ***********.

Face *********** *** *****

*** ***** ********** **** *********** *** has ******** ***** ****, ****** ** cheaper *** ****** ********** **** **** providers. ** ********, *** ***** **** expanded. *** ***** **** ****** ** 2015, **** *********** ** ***** **** or ******** *** ******* **********, ********** tracking, *** ***** ******* *******.

IPVM Image

**** ******* **** ********* ** ****** by ************ ** ********** *** ********* costs.

*******,**** *** ***** **** **** *********** use ** ********* *** ************ ******** *** ** high ******, **** ** ******, ******** over *******, *** ******** **** **********, such ** **** *** ** ************ here.

GAO **** **** ****** ******** *****

***** *** ********** *** ****** *** growing, **** ****** ****** ****** ******** *****, ** **** ******* *****:

**did *** ************* ****** the global facial recognition market revenues or forecasted revenue estimates. However, we did review information from several market research firms—Allied ****** ********, ******** *** *******, *****************, ******* ********, **********, ****** ******** ******, *** ******* ****** ********— and found that the estimates fell within consistent ranges, with only one outlier. [emphasis added]

**** ** * ******* ******* ** have **** ** ***** ************ *** now *** ** **********. **** *** data ***** **** *** ********** ** making ****** *********.

Existing ******* ***********

******** ** ******* **** *********** ******** legislation ******,******* * ** *** ******* ***** Commission (***) ****** ***** ** **** ***********, **** GAO. *** ************* ******** **** ***** ****** ******* ********* that *** ****** ** ********* ********* regarding ********. *** ******** *** *** can *** **** ** ** ***** companies *** ******** ** ******** ********* regarding **** ***********.

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*** ***** **** *** *** ******** ****** ******* ************ *** ***************** ** *****' ******* involving ****** ***********. *** *********:

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*** *** *** ****** ***** *** a**** *** ****** *********** **** ********* report. ** **, *** *** ******:

*. ******* ** ******: ********* ****** build ** ******* ** ***** ***** of ******* ***********.

*. ********** ******** ******: *** ********* that *** *** ********** **** *** context ** * *********** ** * consumer’s ************ **** * ********, ********* should ******* ********* **** ******* ** a ******** **** *** *******.

*. ************: ********* ****** **** *********** collection *** *** ********* ***********.

************, ************ *********** ******* ****** ********** facial ***********:

*****, **** ****** ****** * ********’* affirmative ******* ******* ****** ***** * consumer’s ***** ** *** ********* **** derived **** **** ***** ** * materially ********* ****** **** **** *********** when **** ********* *** ****. ******, companies ****** *** *** ****** *********** to ******** ********* ****** ** * consumer ** ******* *** ***** *** otherwise ******** *** ** ***, ******* obtaining *** ********’* *********** ******* *******

*******, ** ** ***** ************ ****** recognition, **** ****** *******.

*** *******, *** *** *********** **** demographic ********* ** ******* ***** ****** before ********* *** *******:

*** *******, ********* ***** ******* ***** capable ** *********** ********* – ***** often **** ** ********* **** ******* signs **** ** *** ******* ******* ****** ******* ***** ****** ** consumers **** *** ************ *** ** use, ****** ********* **** **** ******* with *** *****.

** *** ***** ****, **** **********, this *** **** * **************, *** law:

*******, ** *** ****** *** *********** best ********* ** ****** ******** ***** requirements, **** *** *** ******** ** serve ** * ******** *** *** enforcement ******* ** *********** ***** **** currently ******** ** *** ***

State ****

*** *** ******* * ****** **** laws ********** ** **** ***********; **********, Illinois, **********, *** *****. *** **** explained **** ** *** ** ****** to ****** **** ******** **** ** standards, ** ********* *** ****** ** comply **** *** **** ********* ********* everywhere.

**** *** **** ****** *********** ********* opt-out ** ********* **** ******** ** Illinois, ** ** *******.

Existing **** *** ******

*******, *** *** *** ******* ***** existing **** ******** ** ******* ********* as *** **** ** *********, *******:

*** ********** *** ******** ********** **** a **** ****** (***-**-***) **** ******** ******** ************* *** consumer ******* ********* ** ******* ******* in ********** *** *** ***********.

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Principles *** **** ***********

*** ****** **** *********** ****** ** open *** ***********, ******* ** ******** uses, ********** ** **** ******* ********* collected ****, ***** **** **** *** most ******** *** ***** ****** **********, and ******** *** **** *** **** protection. *** ********** **** **** ********** should ** *******, **** ******* ********* and *******. **** ****-******* ******** **** should ** ****. **** **** ****** be **** *** * ******** ******* stated *******. *** *** ***** ****** be *******, *** *** ******** ******* consent. **** ***** ****** ** ********** for **** ********** *** *** ********. Also, **** ********** **** *** ******** across ************ ****** ** ****.

****

*** ******** ******* **** *********** ***** ****** ******** * ** *********** *********** *** noted **** ****** ** **** *********** but ***** ** ** ********* *** the ***** ** ****. **** ***** that ***** **** ********** **** ******** and ********* ********* ******* *** ****** others ********* **** *********** ** ********* groups. *** ********* **** ***** ** no ********* *** *** ***** ** this ****. *** ********* **** ******** data ** ***** ****, *** ***** cause **** ** **********. **** **** that ***** ******* ** ** ** “other-race ******” ***** ********** ********* ***** on ****** *** **** *************** ********* than *** ********. *** **** ***** that ***** ******* ***** **** ******* effects ** ******** ********* ** * subject's ****.

Recommendations *** ********** ****

*** *********** **** ******* ******** **** are ****, ********** *** ******** ** avoid ****, ****-******* ****** *** ****, and ********** *** *********. **** ***** that ***** ******* ****, ** ******** in * *** **** ******* ******** sets *** *******, ***** ************ ****. *** ********* **** ********** can ** ******** ** ********* ****. Instead ** ************ ****** *****, ********* can **** ********** ****** ******* ***** across ********* ************.

*** ******:

********** ***** ****** ********** ** ******* equal ***** ***** ******* *********** ******

*** **** *********** **** ***** ********* were ******* **. **** ***** **** algorithms ****** ** **** *********** *** monitored. **** ******* ***-**** ******** ***** help ******** ****. *** ********* ********* modification ******* **** ******** *** ******** concern ********** *** ******** ** *********** bias.

***, ***** *************** *** ********** **** may ** ***********. ** ***** ** hard ** *** * ******* ******** set ******* ********* *******. ********** *** be ****** ** ****** ******* ********* data ** **** **** ** *** diverse. ****, ******** ** ******** **** through ******** *** ***** ****-*******. ** algorithm ***** *** ** ***** ****** on ***** *** (** ** *******) who *** *** ** *** ******** set, ******* ** ** ********* *** the ********** ***** *** ** *** training ****. ****, ******* ***** ***** rates *** *** **** ******** ** threshold ************. ** **** **** ********** less ******** ****-***, ***** *** ******** is * ******** *******.

IPVM ***************

***** *** **********'* ********** *** ****, the ********** **** **** ** ******* in * ****** ** *****.

(*) ******* ****-******* ******** ********** *** used.****'* **** *********** ******** ********* *** ** *** ******* submitted ********** *** ********** ********. **** means **** **** **** *********** *********, ranging **** ****** *** ******** ** most ********** ***** ************ *********, *** missing **** *** *****. *** **** the ***** ** *** *********** ********* the ********** * ******* *****. ****,*********** **** *********** ******* *** ****** available,****** ******** ******* *** ********** ****. The ********** ***** **** ** ******* some **** ** ******* ** ****** to ****** *******, ** **** ** for **** ***** ******** ******* **** medical ******** ** *********** ** ****, etc.

(*) ***-******* ****** *** * ****** part ** ************. ****** ********* *** to *** ***** ****** ** *********, but **** ** ******* *** ***********. The ********** ***** ****** **** ******* low-quality ****** ** ********* *** ********* improved *********** ** ****** ****-***** ***** surveillance *** *****.

(*) ********** ** *********** ***** ******** some **** ** ********* *** ******. Facial *********** ********, ******, ****** ************* determine ** * **** ** ***** it *** ********* ** ******* ********. The ********** ***** ******* ****** *********** users ** ******** **** ******* **** have **** *** ** ** *** public ** ***** ******* **** **** of ******** **** ********** *******. **** does *** ******* **** *** ******** would ** **** ** ****-******** **** as ******* **** *** **** ********** to **** ******** *** *** **** to **** ** ******** ******** ***.

Comments (3)

Hey John, just curious. Have you guys vetted Swiftlane yet? Seems to use Apple's FACE ID for facial recognition. Their door station is literally an Iphone. I have a unit for testing myself, just curious if you have found anything notable. Cheers.

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We had a call with Swiftlane. They claim face recognition is their own and is not Apple licensed. I believe they claim their door reader is built on Android hardware, also. I'll double check and report back.

We haven't tested Swiftlane, but one of the more interesting features is the door station reader doubles as a video intercom.

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I would like to see a review of Blue Line Technologies. I understand it may be the only facial recognition software company with a U.S. patent, not using licensed software out of EMEA.

Blue Line Technology – State of the art facial recognition technology

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