Ear Recognition Examined

By Zach Segal, Published on Oct 12, 2020

Will ear recognition become a viable biometric? Facial, fingerprint, and iris recognition have all gained adoption, can ear join them?

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IPVM investigates the pros and cons of ear recognition compared to face recognition including interviews with leading experts Žiga Emeršič and Professor Mark Nixon and a review of various research papers on the topic.

Executive *******

***** *** *********** ******** from *** ********** *** relative ********** ** *** and **********, *** **** pixel ******* ************ ** well ** ***** ******** by **** *** ******* headware ********** *** ********* use. *** ***** ***** contrasts *** *********** ****** ear, ****, *** **** recognition:

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Ears ******, *********, **** ** ****, ********** ** *****

******** ********* *** *********** has * *** ** benefits *********:

  • **** *** ******, **** ******* *****, and *********. **** ***** ears *** ** **** for ************** **** ***** and ************, *** ** shouldn't ****** ***** *** (left ** *****) ** used.
  • *** ******* ********** ********** ** aging, **** * *****’* *** is *******, *** **** could ** **** *** recognition ******* *****.**** *** ************* **** *********** ** the ***** *** *** indicating ***** *** **** affected ** *****.
  • **** *** *** ******** by **********, ****** ****, ** face *****.

******************** *** **** ********** Chair, **** ********* **** **** *********** with *** *** **** of ********** *** ****** scrutiny ***** **** * promising *********:

** *** ********** ** ears ** **** *** less **** ***** **********. Is ****** **** * cradle ** ***** ***** just *** ******. ****, ears ***’* *****. ****** are ************ ********* ***** privacy, *** ***’* ******* their **** *** ****** and *** **** **** their ******. ** **’* an *********** *****.

****** ************, *** ****** can ** ******** ******* cooperation.

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High *** ******, ******** ** ******** *** *********

******** ********* **** *** recognition *** **** ***** requirements, ** ******** ******** by ******** *** **** tilt, ** ******** ** some ********* **** **** and ********, *** ** affected ** ****** *****. The****************** ******* ********* *********** ** *** X *** ***** *** image (~*,*** ***) ** twice ** ******** ** recognition ** *** * 200 ****** *** * times ** ******** *********** on *** ****** **** were **** **** ** X ***.

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**** ***** ********* ***** where *** *********** ** deployed. *******, ** ****** corridors **** **** *******, such ** ******* ** subway *******, *** *********** cameras ***** ** ******** where **** ***** ******* enough ****** *** ******** recognition, ******** ******* ******* height ***** ***** ** a *******.

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*** **** ******* ***** that *** *** ** resilient ** **** ***** of ********, ****** **** the **** ** ****** (yaw) ** * ***** degree, ** *** ***** is ********. **** ** similar ** *** **** recognition ******** ***** **** a ******* ** ******* down.

*** ********** *** **** organizer,Ž*** ****š*č, **** **** **** vertical ****** ***** *** an ******, *** **** of ** ****** **** yaw/horizontal ****** *****. **** means **** ***** *** requirements *** ** * more *********** ******** ** using ******** ******* **** camera ****** ** *****. The ******* **** ***** that *** *********** ** resilient ** ****** *********/*********** like **** *** ******** but *** ******* *********:

***** *** ****-* ****** remain ********** ********** *** minor *** **** ********** (i.e. ******* *** *********** or **** ******** ******* parts ** *** ****), severe ********** (*.*., **** or *** ********* ******** around **** ** *** ears) ***** ************ *********** degradations *** *** ******** of ****** **********.

******** ** *********:

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******* ********** **** ******** *** center ** *** *** with ***** ***** ***** have * ** ********** point ******, ********** ******* occlusion ** **** **** (such ** *******) ***** have * ***** ****** on ********. ** ********, over-the-ear **********, ********, ***********, and ***** ***** *** completely ***** *** *** making *** *********** **********.

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Lower ******** **** **** ***********

*** **** *** *********** algorithms ******** **% ******** in ***** ********** *** 63% ** **** ********* ones, *** **** ***** accuracy ***** ** ******* of ******* *** ********. On****** ****, *** ********************* **** **** ** achieve **% ********. *** top ********* ** *** 2019***********, ***** **** *** more ************ *********** ******** **,*** **** with ******* ******, ********, hair, *** **** ****** diversity******** **% ********.

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***** ***, **** *** over ***** *** **** 2017 ******** ** **% and *** ******* ********** were **********:

*** ******* ** *** challenge **** **** **** there *** **** ************ progress ***** *** ***** UERC ** ****, *** that ***** ** ***** ample **** *** ******* research ** **** ****.

*** **** ** ***** ear ******** ***** ** why *** *********** ** less ******** **** **** recognition.

No **** ** **** **** ********* **** ******** *******

******* **** *** ****** sizes **** ***** ** sign ** ***** ********* or ******-***** ****, *** in *** **** ***** women ***** **** ****** hair *** **** *** accessories, ****** ** ******** for ******** ************* ** occur.**** ***** ***** ********** (**% ** **% accuracy), *** ****** ******** may **** ****** **** or **** ******** ** average **** *****. ****, the **** ************** ******** *** ******** ********* *** ****** on *** ********** *** found ** **** ** bias:

***** *** ********** **********, gender *** ********* **** the ******** ****** ** identification *********** - *** results *** *** ********* of ***** ********** *** very *****, ***** *** minor *********** *********** *** likely * *********** ** the ********* ****** ** probes ** **** ********

*******, ****š*č ****** **** that ************* ******** ** pedestrians ** ****** ****** that * ******** ** women *** **** *** much **** ** *** many *********** *** ******** recognition, ******* ** **** that ***. ** ********, many ***** **** *********** that ******* ***** ****. He **** ********* **** bad ****/**************** **** **** could ***** ****, **** if *** *********** *** not ********** ******:

** ** *** ****** some ******** ***** **** bias, * ***** *** that *** ****** *** biased ******** **** **** used *** **** *** model (**** ********* *** present).

Lack ** ******** *** *** ************** ******* ******** ****

**** *** ** ********** used *** ************** *** have *** **** ****** adopted.** **** *** **** 7X********** **** *** *********** but *********** *** *** follow ****. ** ********,****** *** * ********* ***** *** *********** to ****** * ********** but *** *** *********** it ** *** **** phones. ** ********,*** *** ******* *********** ************ ***** ******* inaudible **** *******, *** despite ***** ****** *** 2018, **** **** *** been *******.

*******

***** *** *********** *** accuracy ****** *** ** constrained ** ***** ************, it ***** ** **** in ****** ********* **** as ** ******** ** metros *** *********** ** for ************** ** ****** or ****** *******. *******, because ********* *** **’* have ** ** *******, aging, ***** ** ***** heralded ** *** ******* advantage ** *** **** face ***********, *** *** affect *********** (***** **** some ** *** ******* interest ** **********) **** much.

Poll / ****

Comments (6)

*********** **** ******* ***** hand *** ****** ***-******* will ****** ** * dead ** *** ********, this ***** ** * good *********** ** ****** recognition.

** ***** ** * way ** ********* ********* authentication ***** ***** * less ************ ********* **** face ***********.

*********** *******. **** ** you!

*********** *******.

******** ****** ***** *** an ******, *** **** of ** ****** **** yaw/horizontal ****** *****.

** ***** *******/*******/*** '*******' placed ************* ** *** subject's **** *** * clear '*** ****' *** better?

** ***** *******/*******/*** '*******' placed ************* ** *** subject's **** *** * clear '*** ****' *** better?

**********. **** ***** *** ear ****** ** *** camera ******* *****'* ** vertical ***** **** ***.

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