Gender Video Analytics Shootout

By Rob Kilpatrick, Published Dec 14, 2020, 08:09am EST

Using video analytics for gender identification is expanding but how well does it really work? And what personal features are gender analytics effectively using to make their identification?

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We tested gender analytics for over two weeks with five manufacturers' cameras:

Inside, we examine:

  • What physical features most impact gender classification?
  • What was the accuracy percentage?
  • How does hairstyle affect classification?
  • Does clothing style impact classification?
  • Does body type affect classification?
  • How do person and face detection impact accuracy?
  • How does PPF impact classification?

*******

***** ** *** *****, all ******* *** *** most ******* ****** **** length **** *********** ******, making ** ** *** the ******* ********* ** whether ******* **** ** classified ** **** ** female. *******, ***** ********* were ****** *************** *** routinely **** ******** **** an ******** ***** ***** not.

**********, **** **** *** the ***** ******, **** subjects ** ******* ***** and ******* ********** ** male ** ****** ***** on ***** *******, **** no ***** ***** ***** on **** ****.

***** ******* ****** ** manufacturer:

  • ********'* ** ******* *** Appearance ****** **** ******* weighted ****, *********** ******** with **** **** ** female *** ***** **** as **** ** **** rates. ***** ******* **** as **** ****, ****** features, *** ******** ***** were *********** *******.
  • ***** *** ****** ********* similarly, **** ******* ********* hair, *** **** ******** style, *********** ******** ** skirts ** ****** **** often. ************, **** * face *** ********, ****** features **** **** ******* considered.
  • *********'* ****-**** **** *** camera **** ********* ****** analysis **** * **** was ********, ****** ****** which ********* ******** ** person *********, **** **** subjects **** ****** ****. Because ** *** **** PPF ******** *** *** camera ** ******* * face (~*** *** ** higher), ** *** **** likely ** ********* ******** gender.
  • ******* *********** **** ********** hair ** *** ***** and ******* ******** *****, facial ********, **** ****, etc. ******* *** **** the **** ************ ***** sometimes *** *** ******** gender, ****** ********* ******** as * ****** ******* of **** ** ******.

Accuracy ********

******** ** ****** ** be ***** **** (**%+) in **** ************, ** men ********* **** ******* hair **** *****, ***, expect ******** *** ****** who ** *** *** this ********** ***, ** applications ***** *** ********** does *** ** **, accuracy ** ****** ** be ***** ****.

Performance ********

***** ** ** ******** of *** *********** ** all ** *** ****** recognition *********.

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Avigilon *** ******* ********* **** ******

**** ****** *** **** heavily ******** *** *************** in *** ******* ** all ************* ** ********* (discussed *****).

******* *** ******** ******** hair *** ********. ** a ****** *** ******** with **** **** **** were **** ****** ********** as * *****, ********** of ***** *******, **** as ******* ******* ****** hair ** **** *****, shown *****.

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*********, ******** ********** ****** effectively ******* ***** ******* and ********** ******** ******* weighted ** **** ******.

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Dahua / ****** ****** **** ******* ** ****** *********

****** ****** ******, ***** and ****** ******** ****** on **** ****** ********* and **** *********, **** face ********* **** ********. When ***** ****** *********, hair ** **** ******** weighted.

*** *******, ***** ******* weighted ****** ** **** length **** ***** ****** detection, ***** ***** ********** as ****** ** **** was **** *** **** versa.

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****** **** ******* ******** people ** **** ****** when ***** ****** *********, resulting ** ******* ***** classified ** ***** ** hair *** *****.

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

**** ***** **** ********, Dahua *** ****** ****** classification ****** **** **** accurate.

*** *******, *****, ****** considers *** **** **** long **** ** ** a **** **** * face ** ******** *** a ****** **** **** a ****** ** ********.

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*********, ***** ********** **** person ** * **** when * **** ** detected *** * ****** when *** **** ** detected.

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Body **** ******** **** **** **** ** *** *************

**** *** ****** **** types **** ******** **** and *********** **** **** short ** **** **** on * ******. ******, Dahua, *******, *** ******** would ***** ******** ******** as **** ** **** was ***** ** **** back, **** **** ***** female *******.

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Gender *********** **** ******** ** ***** ***

******* ****** *** ******** both ****** ****** ** long ****** / *** PPF ******, ****** ************** was ********** ** ****** distances ***** ******* ******** cannot ** *********. *** example, *** ***** ***** below *** ********** ********** as ****** ****** ** the ******, *** **** at * ********, ******* visible **** ****.

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***** ********* ***** ** get * ****** *** when ********* ****** *** still ***** *** ** guess ****** ** ***** PPFs, ********* ** ***** classifications.

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Skirts ******* ******** ** ****** *** *****

****** *******, ** * person *** ******** ******* a *****, ***** ***** automatically ******** **** ** a ******, ***** *****. Other ************* *********** *********** clothing *****.

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************, ****** ******** ****** more **** ****, ** a ********* ***** * female **** ***** **** was ******** **** ****** it ***** ********* ** classified ** ****, **** a ***** *** ****, the ******* *** ****** classified ** ******.

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Verkada **** ** **************

******* *** *** ******** gender ** *** ****** that **** ********. ** some *****, * ****** was ******** *** ***** to *******'* **** ** people ******** *** *** given * ******. *** other ********* ********** ****** whenever * ****** ** face *** ********.

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Hikvision **** *** / ******* ***** ********

********* ****** *********** *** the **** ******** ** our ******* *** *** also *** **** ***********, only ******** ***** ** be ******** **** ** and ** **** *** (~90+).

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Hikvision **** ***** ******

********* *** *** ****** hair ******* ******** ** other *********. **** ** long **** *** ******* with *** *******'* ****, they **** ********** ********** as **** ***** ****** would ******** **** ** female.

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Versions ****

*** ********* ******** **** used ****** *******.

  • ******** *.*-***-***-**: *.**.*.**(*****)
  • ******** ******* ******: *.**.*.**
  • ***** ***-*********-***: **.***.*******.*.*, ***** Date: ****-**-**
  • ****** ***-******: *.**.**
  • ********* ***-**********-*: **.*.** ***** 180316
  • ******* *****: *** *****

Comments (29)

**, **** ** *** think ***** *** **** that ******* ** *** classifying ****** *********? *** underlying ******** **: ** video ********* ***** ****** their ****** ** ******* a ***** **** **** the **** ** *********** (with **** ***** ** error), ** ******* ** not ******** *** ********* that ** *********?

******.

*****, ****** *** **** first ******* *** * good ********. ** ***** on **** ********, **** are ****** ********* ** use ****** ********* ***? Is ** ********* ************? Or ** ** "**** me *** ******* *" or?

* *** ** **** seems ** ** **** entertainment (*.*., *********** ** demo) **** ****** ***. To ** *****, * am *** ******** **** some ***** **** *** of ****** *********** *** I ******* **** ** not.

* ***** **** *** classifying ****** *** *** time ***** ** ****, but ** ***** ** better ** ***** *** a ******** "*******" ********, instead ** ****** *** classifying. ** ** ****** now, *** *** ****** for **** ** ******, but ** *** **** to *** ***-********** ****** detections *** *** **** see **** ** ******* all ******* ***, ** you're ******* ** ******, male, *** ************ *** at ****.

****** *****. * *****, the ****** ****** ******* data **** **** ********** and **** **'* *********, just *** **. **** like * ******* ** an ***** ***** ** sometimes **** ** ******* information **** * **** level ** ******** *** sometimes ***** ***. **** bad **** ** ***** than ** ****. ******** the ***** ** ****** specifically *** "*******" **** becomes ********* **** **** approach.

** **** **** ***** an "************" ******** *** some **** *******, **** as ******, *******, **** covering.

** *** **** ********* used ** ****** ******* of ******, *** **** being **** *** ***** with * ****** ** specific ** ******** (****, with *******, **** *****, moving ** * *********), and **** **** *** some ** *** ********** depending ** **** *** are **** ** *** from * ***** *****.

*** ***** ****, ** course, ** **** ** have ****** ****** ** make * ********** ******** on ***** *******, *** the ******* **** ***** points *** ** **** many ***** *** ****** make * ******* ********, so ** *** ******* "Undetermined" ** * *** to ********** **** *** product ***** ** ** unable ** **** * hard ********. **** *** be *** ** *** resolution, ******* *** **** lighting, **********, ** ***** factors.

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**** **** ** **** a ****** ******** *** both **** *** ******, where ****** ***** **** general ********** ******* **** consideration (********* ****).

** ** *******, *** can *** ** **** scene *** ******* ********** my **** ** ************ gender, *** ** **** to *** *** ********** and *****, *** **** using * ****** ******, accurately ********** ** ** Male (****** ***-****** ***** here).

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*******, * ***** **** gender ************** ** *** of *** ***** ***** we (*** ******** ** well ** ******) ***** likely *** * *** of *********** ** *** next ******* *****.

********** ********** ** ** Male

**** **** ********** **********? :)

** *** **%, *** then * *** **** testing ***** ******* * Pumpkin ***** ***** *** it **** **** ** 51%.

***** *** ** ****** purchases

********* ********** *******!

************************** ***. ****. *** I **** *** * few ******* ***** ****** myself ** * ****.

* ***** **** **** is ********* ** ******* for ** *** ****. I ***** ****** ************* have * ******** *******/******* than ** **** ** question **’* ********. * know ***** ****** ** any **** ** ****** contributes ** * ******* that ***** ** ****** any ****** ** **** system(s). * ***** ******* and ****** ** **** ilk *** **** ****** providing **** ********** ******** versus **** ******* ** their *********. ****** ********* seem **** * ***** category. * ***’* ****** what ***** ** *** get **** ** ******** classifying ******.

*** * ** ******* for * ****** **** a *** ***** *** blue *****. ***** **** to ****** *** ******* for ***** ************ *** be ****** *** *** sure *** **** ****** are ***** **** *******. This ** ********* **** can ** **** **** Avigilon's ********** ******.

* ***** ** *** all ***** **** * system **** ***% ******** would ** *** ****. But ***** **** **** will ******** ***** ****** (for * *** ** reasons), *** ******** ** to **** **** ** the **** **** ******. It *****'* **** *** system ******* ** ** solves **** ******* **% of *** ****. ******** search ** * ****** usage ** ****** *********.

** *****'* **** *** system ******* ** ** solves **** ******* **% of *** ****.

*****, ** ***** '****' 90% ** *** ****, as **** ** **** everyone *** ******-************* ********.

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****: ** ***, * think **** ***** ***** people *** ***** ****************. It's *** ***** *** computers ** **** ********, it's ******* ** *** viewer ***** ** ** and ****** **'* ********* wrong *** ******** *********.

** **** ******* ****** with **** * *** years ***. ** ******** misidentification *** **** *** system ********** *** ***** Leader (* **** ** his *** **'*) ** a ******. ** *** some ******* *** ** appears **** *** ********* gave **** ***** ****** to *** ****** *****. He's **** **** **** our ******* *** ***** 2 *****, *** * still **** ******* ***** it.

*** *** **** ** post ***** ** **** topic *** *** ******** involved, ** *'* *** even ***** ** ** it.

*** ** *****. ** one *** **** ** tell ** **** ******** to ***.

* ***'* ********** *** there ** * **** to ******** ** ***. I **** **** ******* manufacturers *** * **** while **** *** *************** cheapen *** ******* ********* package **** **** ** not ****. ***** ** works **** ********** * feel **** ****** *************** are **** * "****, why *** **** ***" feature **** *** *** for, *** ****, *** does **** **** **** good.

*** *******, * *** years **** ** *** a ***** ** ** otherwise ********** ****** *********** platform **** *** ******** rejected. *** ****** *** not ******* *** ****** recognition ******** *** ******. The **** ** ******* identification *** **** **** for ****. *** ******* reason ** **** * single **** ******* **** member *** ***** ********** as *'* ***** **** this ****** *** * 5'11" ****. **** ****** was *** *** **** cause. **** ***************** ****** a ******* **** ** the ***** ** *** the ****** ***** ***** of *** *******. ***** this *** *** *** sole ****** ***** ************* it *** ********** *** wrong ******. *** *** and ****** ************** **** has ***** **** ******* from *** ****** *********** analytics *******.

* ***'* ********** *** there ** * **** to ******** ** ***

*** *** **** *** cases * **** **** are *** ****-***** ****** and *** ****** ********* applications (***** *** **** primarily ****-*****).

***, ** *** ** the ***** **** ** the **** ***** ** analytics, ***** ******* **** be **** ** ******* more ******* ***** ********** attributes ** ** ******, and **** ***** ** figure *** **** ******* of ** ******* *****, not **** **** ***** are ******** ******* ** the *****.

* ***** *** **** a ****** ***** ****** based ** * ****** for ****** ***************, ** I ***** ** ** unlikely ** **** ******** enough **** ** **** to ******* * ******** focused ** **** *** specific *** ****. *** when *********** *********, ** can ******* ***** ************* for *** ****.

****** *** *********** ** the ******* ** *** wacko ****** *** ****** after ***. *** *** test * *****?

***** **** ******* ** rather ***********, * ** think ***** ******** ******** to "**********" **** ****** identification ***** ** * better **** **********.

"**** **** ******* * red ***** *** **** jeans" ** ********* **** can ** ******** **** a ******* ******** *** less **** **** "* woman ***** ** ******."

* ***** ***** ** wallet

* ******** ****** ********?

*****, ** *** ***********. Should **** ******* ***** before ******* ****.

πŸ˜‚*** *****. ***** ** one **** *** **, i ****. *** **** comes ** **** ** that ******* ***** ** the ***/*** ** ******** throwing * *** *********** as β€œ**’**” πŸ˜‚πŸ˜‚πŸ˜‚πŸ˜‚

***** ** ** ******** to *** **** **’** thru *** ********* ** see **** ** **********? πŸ˜‚

*** ****, ** *** system **** ** ***** (in ***** ****) **** will ***

*** ******* *** *** mentioned *** *** ****** for **** ***** ********* based ** *** ******* in ***** ** *** monitor.

** *********** ***, ******, race *** ***** ******** β€œstyle” **********, *** ***** display ******* *** ** thousands ** ********** ********* messages.

** ** **** ** would ********* ********** ***********, golf ***** *** ******* pads.

** ****** ** *** person ** *** **** available? ****** *** * friend.

*** ********* ******* ***/****** as * ***** ** the *********** ****** *******. With * ****** *********** function **** *********** ******, the ******* ***/****** ****** only **** ** ** good ****** ** ******** a ****** *** *** a ********** *** ** images **** ***** ** further ****** *** ******. Once ** ***** ** selected **** ******* ****** results *** ****** ******* converges. *** ****** ** success ** ******** ** not ** **** ******* accuracy *** ** *** quickly ** ******** *** find **** **** *** looking ***. *** *** same ******, ****** ******* are **** **** ******** in ** ********** ****** that ** ********* **** live ** ******** *****. As *** ********** ******** such ** ******* ******** search ** *********** ********, more ******** ****** ****** will ** ****.

** *** ******** * search *** ** ****** key *********, ******** *** search ********* ***’* **** it **** **** ** has ******* *******.

* ****** *** ******, red *****, ***** *****. 20% ** *** ***** in *** ***** *** missed. **** ******* ******** it ****?

*****. ****** *********** ********** either **** ******* *** mathematics ************ ** *** NOT ****** *********** **********. That ******!

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