Single Frame Gait Recognition From Michigan State and Osaka University Examined

By Zach Segal, Published Oct 01, 2020, 10:23am EDT (Info+)

Gait recognition has the potential for accurate identification at a distance, even without seeing the face but suffers from a number of limitations. Single frame gate recognition is an alternative approach that aims to improve on these.

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IPVM spoke to researchers from Michigan State University and Osaka University that seeks to solve these problems that we addressed in our gait recognition review.

Executive *******

*** ******** **** ******** ***** *** Osaka ********** **** ** ******* ******** accuracy **** ***** ****** ****** ** real-world ************ **** ************* ******* ************* load *** ******* ************.

*** ******** ***** **** ******** ******* on **** ***** *** ********, ******** clothing, *********, *** ******** **********. *** Osaka **** ***** *** ******* ********* and **** ******* * ***** **** uses *** ***** ** ******** ** entire **** ******** ******** *** **** comparison ** ****** ****** ***** ** different ****** ** * **** *****. Both ***** **** **** **** **** believe ***** ** ******* ********** *** the ****** ** *** ***** *** that **** ***** ******** *** ****** of *****/*********** ********* **** ********, *****, carried *******, *** ****** ***** **** have ******* *********** **********.

Why ***** **********?

****** *********** **********, ***** *** **** use ****** ******** *** *** ** done ** * ****** *****, ****** them *********** **** ********, **** *************** intensive, *** **** ** **** ** situations ***** ******* ******* *** ********.*********** ***************** * **** **** *****, **** and ***** ***** (~* ****** ** footage). **** ***** **** *************** *********, and *********** ** *** **** ***** where ******* ** ***** *********** ** other ******, *******, ******* ******, ***. They **** *** **** ************* ******* learning **** ****** ** **** ***** intuition, ******** ***** *********.

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MSU **** ******* ***-**-*** *************** *********

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*********** ***** ********** ***-**-*** ****** **** *********/**************** ** ****** ** ********* **** they *** ** **** ******** ** certain ****-***** **********, *** ** *** on ****** ******, *** **** **** say *** **** ********* **** **** and *****-***** **********.*** ********* ***** ** *********/************* *********** ********** ****/***** (**** ********), *********/****** data (**** ******* *** ***********), *** pose/dynamic **** (**** ******* *****). ** then ******* *** ********** **** *** averages *** ********* **** **** **** frame. ****, *** ********* *** **** data *** **** *** ********. *** canonical **** ** ******** ** **** frame, ** *** ********* *** **** with ********** ******** ** * ***** instead ** * **** ***** (~**% worse). *** ********* ********* ********** **** overall ** ******* (~**% ** ****** conditions *** **% **** * ***). Importantly, ***** ********* *** *** ******* affected ** ******* ** ********** *****, even **** ****** *** ****** ***** is ********* *** ************ ******* *** set ** *** * ********* *** most **** **********.

********* ***, **** *** ******** ***** ****, explained ** **** **** *** *** similar ********** **** **** ******* **** traditional ******* ******* ** ** ***-**-*** and **** ********-***** ******* ** *****-****. Liu **** **** **** *********** ** lost **** *** *** ** ***** is *******, ******* ** ****** ** accuracy, **** *** ********* **** *** suffer ******* ** ** * ****** step. ***********, *** ****** **** *** computer ****** **** **** ** *********** is ********* ******* ** ******** **** type ** ********* *** *** *********. This **** *** ******** ******** ***** part, ******* ** **** ******** ***** on **** ****** ******* ** *****. Liu **** *** ******* ********** ** model-based ********** **** ** **** ** reach ********** ******** ******:

* ** *** ******* ********** ***** methods *** ******* **% **** ********* rate **** *.*% ***** ***** ****.

** *** ** **% *** ** 0.1% *** ** * ****-******** **** set, ** **** ** ******* ***[*****-**-***-*** model *****] *********** ** ** ***** 100 *****. * ** *** ***** multi-view [**] *** ***** **** ***** of ********* ***********[*** ***** ** ********** based].

OU **** **** *************** *** * ******** ***** ******** *********

******** ********** ******* ********** **** ******************* *** **** ***********, *** *********** **** * **** ******** ************ ******-***** **** *** ******************* ******* ***** ******** ***** *** **** **** accuracy.**** *** **** ********** ******** *** ***** ** * gait ***** * ***** ** ****, then ******** * **** ***** **** it. **** ****** **** ** ******* frames **** ********* ****** ** * gait *****. **** ******** ****** ******* than ***** ***-***** ********** *** **** able ** ******* **% ******** **** 1 ***** ** ******* ******** (******* confounding *********).

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**** **** ***** ********* ******* ***************/********** ********* ***** ** ****, but ******* ** ******* **** *** footage, **** **** ************ *** ******/***********. They ***** ***** ********* ** ********** covariate ****/***** **** *** ****** **** for ************** *** ** ******** ***********. On *** ***** ********** ***** ********** data *** **** **** *** ****** horizontal ******, **** ******** **% ********, a **% **** **** ******** ********** (note *** ***** ********** ********* *** pre-processed **** ** *** ******** ***** team’s ********* ****** *** **** *** testing). **** **** ******** ******** *********** on *** *****-*, ******* ***% ******* authentication ** ****** *** *** ******* individuals *** **% ** ****-******* ********. This *** ***** **** ** *** use ** *************** *** **** ******** as **** ** *** ****** ** data *** *** ***. ***** **** did *** ******* ********** ******, *** technique ***** ** ******** *** **** use.

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********** **** *** ******** ** *** Osaka **** **** **** ********** **** Professor ***, *** ***** ******** *********** techniques *** *** ** **** ** perform ********** ****** *** ****-**** *********** in ****-***** *********:

*******, ** *** **** *********, ** have *** *** ******* **** ******** [95% **, **% ***** ********].

**, ** **** ** ******** **** variation ****, **% **** ********* **** with *.*% ***** ***** **** ****** to ** ********. *******, ***** ************ multiple *******************, ** ** ***** ***********.

**** ********* ** **** **** ******** techniques **** *********** ********** ******* ** deep ******** *** **** ******** ******* learning ***** ****** ********:

** ******* *** **** ********-************ **** **** ********* **** *** traditional ******* ********-************ ** **** *********** ********* ********* to ***** ******** ************** *****.** ****, ** **** ******* **** many **** ** **** *********** ***** on**** ********, *** **** ***** *********** to *** *********** *******.

Commercialization ***********

***** ******* **** *** ***** ** commercialize, ***** ********** *********** ****** **** the ******** ******, ***, **********, *** US ********** *** **** ** ********.

********* *** **** **** ** *** no ******** ** *************** *** ******** a *** ** *********** **** *** have **** ******** **** ******** *********:

******* *** *** ********** ******* ********** funded ********. *** **** **** ** put ** ****** ** *** ******** and *********** ****, ** ***** ** push ********** ** *******/******. ***** **** faculty *** *** **** **** *** research/technology *** **** ** ****** *** that ****

*******, **** **** ****** ** ** investigations **** *** ******:

******** *** ************** ***, *** **** verification ****** *** ******** ************* *** been ***** ***** *** ** *** National ******** ********* ** ****** ******* since ****. ** **** **** ****** expert ********* ** *** ********* ** the ******** ****** ********.

*****'********* ***** *** **** ******* ** have ***** ******* **** *********** **********, but ****.*. ********** ** ********* ** **** scientific ************** ********* *********** ** * ********, which ***** **** ***************** ** *** technology. ** ********, ********** ****-******* *** increased ******** **** **** *********** *** lead ********* ** ***** **** **** as ** *********** ** ********** ** face ***********.

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