Facial Recognition Guide

By IPVM Team, Published Mar 15, 2021, 11:24am EDT

This guide explains the fundamentals of facial recognition.

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Inside we cover:

  • The Use of Datasets in Facial Recognition
  • Dataset Creation Methods
  • Common Dataset Problems
  • Prominent Public Datasets
  • Changes in Public Datasets
  • Architectures and Design Considerations
  • Open Source Facial Recognition Models
  • Options for Sourcing Facial Recognition
  • Legal Considerations For Facial Recognition

This is part of our new Video Analytics Course starting in March.

Executive *******

****** *********** ** ********* on ****-******* ******** ** facial ****** *** ******** their ******. *******, ******** have ******* ******, *********, ethical, *** *****.

****** *********** ********* **** also ****** ***** ********* source, ******* ***** **-*****, open-source, ** ******** **** another *******, ******* ** the **** *************, *** support ************.

******** *** ****** *********** is **** ** *******, in **** ******* ******* are *********** *** ********* use ********** **********, *** also ****************** **** ****** ** surveillance ***-***** **** ***-*******, where ****** *****, ********, uncooperative ********, *** ***** factors ****** ********.

******** **** ***-*********** *** ********** *** legislation**** ****** *********** *****/*** ****** recognition *** ******* ** used. **** ** ******* to * ******** ** public ********, ********* ********** with ******* ************, *** a ******* ******** ** demand *** **** ***********.

Options *** ******** ********

**** ******** **** ******** used *** ***** ************ analytics ******** *** ******* are ****** **** **********, open-sourced, ** ***********.

*** ****** ** *** dataset ******** ** ****** primary ** * *******, each **** **** *** cons:

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********** ********

********** ********, **** ** national ** ***** ** passport ******, *** ************ **** ** * country's **********, ** ******** larger ** **** ******* visa *********** ******.**** *** ********* ****-******* images *** *** ********* only ********** ** *****-***** manufacturers **** *** ***.

*********** ********

*********** ******** *** ******* by ** ** ****** of * *********. *** fundamental ********** ******* *********** and ****-****** ******** ** that *********** **** *** not ******** ********. ** a ******* ******* ** use * *********** *******, they ****** **** **** of ****** ** ***, how **** ** ***, and *** ** ******* them.

******* ******** ****' *** proprietary ********** ********* *** ****-********* to ******, ********** **** specific ***** ** ****** and ***-** ******* ** they ***** *** ******* or ******** ******* ******* or ********* ** *********.

****-****** ********

****-****** ******** *** *** most ********** ******, *** they *** ******* **** the ******** ******* *******, occasionally ********** ** “*****”, smaller ** **** **** large **********-******** ****, *** many *** ********* ****** which ****** * **** of ********* ******* *********** are *** ** ******* as *** ******* ********** in *********, ***, *** general **********. ** ********, due ** *** *** the **** *** *********, sets **** **** ***** down ******* **** ******* laws.

5 ****** ******* ********

* ** *** **** common ******** **** **** recognition ******** ***:

  • *** ***** ****** ******
  • * **** ** *********
  • **************** ****
  • *********** ********* ****
  • ********** ****

**** ***** ***** ******* dimensions ******* ****** ******* problems:

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*** ***** ****** ******

******** **** *** *** small **** ** ********** algorithms. ********** **** ** learn **** * ****** is *** **** ****** despite *****, ********** ******, clothing ******, ****-***** *******, lighting, *****, *** ****-** while *************** ******* "*************" which ******** * ***** amount ** ****.*********** *** ************ ************ ** **** ** millions** ****** *** **** ****** than ******* ****. *** only ******** ** ** collect **** ******, ***** is ********* ** ** combine ********.

* **** ** *********

* **** ** *********, with ******* ** ******** like *********, ***, *** gender,***** ** ********* ****** ****** ******** in ******** ** ***** accuracy. *** ******** ** to ****** * *** with ********* ** **** or ** **** * diverse ****** ** ** existing ***.

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**** **** ** *** Representative ** ***-****

**** **** ** *** representative ** *** ***-**** causes ********** **** **** well ** ******** ** perform ****** ** ****-***** surveillance *******. ************ ***-***** often ******* **** ******* with ***** ******** ******, while ***-******* ****** *** generally ***** ** ****-******. The ******** ** ** include ****** **** ********* the ********, ******, *** scenes ** *** ******'* intended ***.

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*********** ********* ****

**** ********* **** *** internet ** ******** ********* without ******* ********* **** to****** *******************, *** ***** ***** in **** *****, ******* in ******* **********. **** social ********** ***** ******* the ****** ** ***-***, but ***** *** *** be ***** **** ***** data ** ***** **** for **** *********** ** default *** *** *** read *** ****-********** **** agree **.

**** ****** ****** ** "consent" ** ****** ***** data ********* *** **** recognition ******* ***** ***** of **. *** ******** is ** ******* ******* to ******** **** ***** data **** ** ********* for **** *********** ** they ****** ** ***-**.

********* **** **** ** comply **** ***** *********** face *********** ***** *** risk ** ***** ************ blocked **** ***.

**********/***** ****

**********/***** **** ****** ** algorithm ** ***** *********** and ***** ** ******** ***********. *********** ******* ****** dataset ******** **** ****** or ********** ****** ******** data ** *** **** that *** ******* **** mislabeled **** * *******.

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** ** ****-********* ** fix ******* ***** ***** in * ******* ***** to ** ******* *** proper ********.

9 ****** ****-****** ********

****-****** ******** *** ******** used ** ***** ************ for ******** *** *******, but ***** *** **** variables *** ******* **** and ****:

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***** ****** **** ******* ************ set **** **** **** 10 ******* ******, *** ** **** contains ****** **** ***********. The ******* ********* *** with * ***** ** diversity,*** **** *, ******** *.* ******* images *** ******** ********, celebrities, *** *********** **** a **** ** **** diversity **** ********* **** sets, *** ** ***** not ************** ** *** general ********** (** ** **% *********) ** *** **.

*** ******* *** ***** contained ****-***** ****** *****,***** ************* (****), ** ** longer ***** ** ******** ******* ****, *** ** **** to ******** ****************** *** ** ***** downloadable ******* ***** *****.

***** **** ********, *** the ****** ******** **** available *********** *****+% ************** ****, ********* ** *** diversity *** ********.

Face ********* *****, **** ****** ***********

****** *********** ******* ** faces ** ** ******** first *** **** *** face ** **** ** a ****** *********** ********* to ** ******** *** matched.

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***** **** ********* ** challenging, ** ** **** easier **** ***********. **** detection ******* ********* ***** not ********* * ****, rather **** *********** * non-face ****** ** * face.

Options *** ******** **********

****** *********** ********** **** commonly **** *** ***** surveillance ***:

  • ****-*********/**** **-*****
  • ****-******
  • ********* **** * *********

**** **** *** **** and ****, ***** ** technical **********, **** ** create, ****, ************ ** design, *** ******* **** design:

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****-*********

****-********* ********** *** **** by ********** ******* ** purchased ** ***** **** open-source. *** *********/************ ** transparent ******* *** ********* knows *** **** **** and *** ******* **** the ******, *******, **** take **** *** ********* talent ** ******.

****-******

****-****** ********* ***** **** turnkey ***-******* ********** ** customizable ******** **** ***** one ** ****** ** algorithm ***** ***** ****** of ******** *************, ******** methods, *** ****. *** structure/architecture ** *********** *** takes **** ********* ****** and **** **** ****-*********, but ********** ********* **** limited ******* **** ********* design *** **** ******* some ********* *****.

******** **** * *********

********** ******** **** *****-***** developers *** *******, ** less **** *** ********* talent *** ****** **** self-developed **********. *******, **** cost ***** *** ***** do *** **** *** they **** ******* *** can **** **** *******. In ********, ********* ** license *****, ********** *** end *** *********, *.*.**** ****** ****** ** XNOR.ai's****** ********* *********.

Algorithms *************

*** ************* **** *** video ************ **** *********** have *******, **** ********* using **** *** ***-***** machine ******** (*** ********* ********* ************ *****) ** ******* **** learning *********** ****** ******** (CNNs). *** ************* ***** higher ******** ******* *** critical ******* ** ***********.

**** ******** *** ****** of **** ********* ** breaking *** ***** **** into ***** ******** *** summarizing **** ******* ** create * ******* ***** that ***** *** ******** information. *****, *********** ******** of *** ***** *** run ******* * "******/******" which ******** *** ***** into * ****** ***** of *** *** ******* image, ***** ** ****** convolution.

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****, *** *** ******* image ** ******* ******* through *** ******* ******* of *******, ***** *********** sections *** ********* **** squares ** ** **** smaller *****, ** ****** the ******* ***** (*** pooling) ** ******* ***** (average *******).

***** ***** ***** ******** the **** ** *** image **** ** ********, the ******** ********** **** is ******** *** ****-**** analytics ** **** ******* than ***** *** ** heuristic *********, ***** ********* GPUs. (*** ********* ********* ******** ***).

Loss *********/********* ******** ******

**** *********** ********** *******-*********** ******* ******** ****** training. ********** **** *** ******** themselves ****** ******** ***** these ******* *** ********* loss ********* **** ** different ******* ******* ********** will ****** ********** ***********.

*** ****** ****** *** increasing ******** *** ********** is******* ****, ***** *** ********* uses ** ****** ********* image, * ********* ***** of *** **** ******, and *** ***** ** a ********* ****** ** automatically ****** *** ******* it **** *** **** person ******** ** * different ******.

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******* ** ******** ******* Margin ****** ******* ****** ***** a ********** *******/****** ** added ******* ****** ** different ******, ** *** difference ******* ********* ****** is ********* ******* ********* the ********** ******* ********* images ** *** **** person.

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*******/******** **** ********* *** newer *** **** ******** used ** ******** ******** and ********* ****** ** training **** ******** ****** recognition ******. *******, ** is *** ***** **** specific **** ******** *** particular ************ ** *****, and ***** *** *********** be ** ********* ** performance, ** *****.

Open ****** **********

***** *** **** ******* for ****-****** ********** **** take ******* ****** ** skill ** *********. **** algorithms **** ******* * few ***** ** **** while ****** ******* *********** knowledge ** ********* ************ from ***** ************.

  • "****************":*** *** **** *** running *************** *** ** copy *** ******, ** a **** *****'* **** to ********** *********** ** use *** *********. *** developers ** *** **** what ******** **** ****, but****** *****"****** ******** ***** [****] pictures ******* **** ******** (celebrities, ***)".** *** ** **** MIT *************** ****** ** *** or ****** *** ******* and **** **** ** as **** ** ****** is *****:"******* ********** *** ****** to ***, ****, ******, merge, *******, **********, **********, and/or **** ****** ** the ********"
  • ************:*** *************** ** ******* ResNet ************* **** ******* and ******* **** ********* including *******, **************** ***** ********* ****** ******** ******** ** 2020. ************ ** ******* with **-*****-******** *** *** no-longer **************-*******. ** **** ********* *.* *******, ***** ********* ** *** *** license*** *** ********** ***** when ******.
  • ********: ** ***** ***** created ** ******** ***** University **********. ******** **** early ****-******** ********** *** is ***** ** ****** and *****. ** *** been *** ** ******* ******* ****-****** ******. ******** *** ******* to *** ***, **-*****-********, *** *** **************** *******.** ************* ** ******** ****** University *** ******** ***** the****** *.* *******.
  • ***********:********* ** **** ** Imperial ******* ****** ******** submitted *********** ** **** **** is *** **** ***** ********.*********** *** ********* ******** built **** *** **-*****-******** and **** ***** ***** Celebrities ********. ** **** the *** *******.

Purchasing ****** ***********

***** *** * ****** go-to-market ******* *** **** recognition:

  • ******* *******
  • ********* **********
  • ***** *** *******

******* *******

**** *********** ******* **** finished ******** ** ***** models *** ***** ** create ***** *** *********/****. NVRs, *** ********, *** access ******* *********, **** embedded **** *********** ****** them ****** ** *** but **** ************. **** are ****** *** *** easiest *** ** *** face ***********, ******* *** system ** ******* *****. Companies ***** **** ***** manufacturers **** ********* *** Dahua ** ********* *********** like ********* *** ********.

********* **********

********* **** **** ***** algorithms, **** ** ****** AWS ***********, *********, ***, NTechLab, ******, **********, *** SenseTime, *** ********* ****** their *** ****** ********* or **** *** ****** using ***** *********. **** is * **** ****** for ********* **** **** to ****** * ****** solution *** *** * custom **** *********** *********.

********* ****** *********** ** common ** ***** ************, embedding ********** ** *******, recorders *** ******/**********, ****** than ********* *** ********* into ******** ***** ***.

***** *** *******

* ***** ****** ** these ********* ******* ***** APIs, (*.*. ****** *** Rekognition), ***** *** ********* is ****** ** *** cloud, ******* ** **-*******. This ** ********* * high ********* **** *** less ****** *** ** easier ** *** *** does *** ******* *** hardware *** ********** *** analytics (*** **** ********* ********* ******** ***).

Pricing ****** *******

*********** *** ******** ***** can ************* ******** *** final **** ** *** analytics ** *****. *******, the **** ** *** a ****** ********* ** algorithm ******** ** ***********, as *********** ******** *** logistical ****** **** ***** problems *** ****.

***** **** **** ******** face *********** ******* *** cost ******* ** $*,*** - $*,*** *** ******, low-cost***** ******-***** ********* *** available *** ***-$***.

Assessing ************ ********

** ** **** ** know *** ******* ** a ************'* ********* ******* they ***** ****** ********** statistics, *********** ******* (**** IPVM ****) ** ****, and ****-***** ***** ** less ******** **** *** testing ** **** ********. Vendors **** ** ****** with **********, **********-******** ******* like **.*% ********:

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***********, ***** **** ****** recognition ********* **** *********** testing ** **** (** Dept ** ********)**** ******* (****)***** ***** ****** * generic "***** ****" ********* details ** *** ****** company *** ********* ***** version, *** *** *** clear ** ********* **** is ****** ** ************ available. ** ****, ********* can ****** ******-********** ****** to ****, **** *** not ** ********* ****** to *** ** ****** video ************ ********.

*******, *** ***** ****** accuracy ** ******** **** may *** ******* ****-***** scenarios, ***** ******** *** uncooperative/looking ****, ******* *********** makeup ** ***********, *** cameras *** ***** ******* at **** ******, *** subject ****-******:

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*** ***** ******* **** real-world ******** ****** **** dataset ******* ****** ** hard ** ****** ********** prior ** ********.

Legal ******

**** *********** *** ****** controversial **** ******** ***** over *** *** *** the ******** **** ** create **.******** *.*. ****** *** cities**** ***** ********** *** GDPR ** *** *.*. places ************ ** **** recognition. *** ** *** strongest ******* *********** ** the *.*. *** *.*. that ****** *** *** of **** *********** *** BIPA *** **** ************.

****

*** ******** ********* *********** Privacy ***(****)******** ******** ******* ******* to *** * ********'* face. ** ***** ***** a **** ******* ******** written ******* *** *** purpose * ***** ******* with ******* ******* ** $1,000 ** **** ********** ******** *** *********’* attorney ****.

****

** *** *.*.,******* **** ********** **********(****)********* *** ********* ******** ***** *** ******* be ********, ****, *** stored, *** *** ** exception *** ****** ******** use-cases. **** ********* **** Recognition ******* ******* ** a "*********** ****** ********". It ********* **** ******* needs ** ** ********, and, ***-********** ***** ** be ** **** ** consent. **** ************ *** ******* ****** *** ******** data ***“*** ***** ** ** forgotten”(** **** *** ******** data *******) ***** *** be *********** ** ********* for ******* ****** ** comply ****.

Public ******** ***** ***** ****

********* ******* *** ***** changes **** *** ** datasets ***** ***** **** and ******** ******* ********* that *** ****. ******, Google, *** ********* ************ ******** ** ***** action ******* ******** **** *** use ** ****** ********* ** ************, ***** *** *** taken ****. ***** ******** have **** ***** **** with ****** ***********, ********* the***** ****** ******** ******* 100m (****)*** *** ********** ** Washington******** *******.

Comments (5)

******* *** ********* **** surely ***** **-******* ********* and ****** ** ************ when ******** ******* **** powerful (*** ********** **** NVR *** *******) *** AI ********** ****** **** efficient *** ********.

*********** ******* ******* ** interested ** ******* ********. Guy ****** ***** ******** papers:

Agree
Disagree
Informative
Unhelpful
Funny

***** *** *** ********** BIPA ** **** ******, which ** ** **** is *** **** *********** facial *********** *** ** the ***** *****. **** and **** (*** *** future *********) *** ** restrictive, *** ****'** ******* near ** *********** ** BIPA. ** ******** ** the ******** *****-****** ********, BIPA *** ****** ****** to ****** ** ******* its**** & ******* ***** *** ************ **** **************** ** ********. (************, the**** ******** ******** ********* ** ********...*** now.)

Agree
Disagree
Informative
Unhelpful
Funny

...** ****** *********** **** should ** ******** *** more ***** ***** ** tech ******? ** ** can **** ** “**** and ******* ***”? *** surely ****...

Agree
Disagree
Informative
Unhelpful
Funny

...** ****** *********** **** should ** ********

****, **** ******* **** to ***********, *** ****** Illinois.

Agree
Disagree
Informative
Unhelpful
Funny

******** ** *******? * err ** *** **** of ******* **** ** comes ** *******. **** old ****** **** ***** required ****** *********** ** the ****.

**** ****-**** **** ****** recognition ****** *** *** of **** *******.

***** **** ******* ** you ******** ****?

****

*** ******** ********* *********** Privacy ***(****) ******** ******** ******* consent ** *** * resident's ****. ** ***** using * **** ******* explicit ******* ******* *** the ******* * ***** penalty **** ******* ******* of $*,*** ** **** as ******** ******** *** plaintiff’s ******** ****.”

*** “...******** ******** ******* consent... *** ******* ** civil *******”? *** ** this ************?

Agree
Disagree
Informative
Unhelpful
Funny
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