By Zach Segal, Published Sep 14, 2020, 10:14am EDT
Facial recognition faces increasing ethical and political criticisms while masks undermine its effectiveness. One alternative is gait recognition but how realistic is using gait?
IPVM investigates gait recognition's pros and cons compared to face recognition including interviews with a leading gait expert Professor Mark Nixon and a review of various research papers on the topic, explaining:
Gait Recognition Fundamentals
Accuracy vs Facial Recognition
Factors that Affect Gait Accuracy
Benefits of Gait
Gait Computationally Intensive
Enrollment Hard, Data Lacking
Appearance Vs. Model
3D Images and Angle Simulation
China Watrix, Only Surveillance Gait Seller
Dahua Claims Record Accuracy
Gait Recognition Use in Court
Gait for Authentication/Access Control
Other uses for Gait
Executive *******
**** *********** **** * person's ****** **** *** movements ** ******** **** but ** ** ** in **** ******* *** today ** ** **** to ********* *** ****** it ** ** **** be.
******* *** ** ** not ** ******** ** face *********** *** ***** supplement **** *********** *** other ********** ******* ** has ********* **********. **** also *** ** *** be **** **** * wider *** **** **** recognition, ***** ** ****** with *****, *** ** hard ** *****. ***,** ***** **** *** company ******* **** *********** (China's******) ****** *** ******* ** gait *********** ********** *** challenges ********* ***** ********, significantly ****** ********* *****, camera ******* ******, *** difficult **** **********.
******** *** ****** ** the **** ***** ** about * *, **30 − ** ******, **** ***** ********* **** ***** ** ** ************ for good individual recognition. Increasing the number of consecutive frames for classification improved the results. The expanded time of analysis was required because body ***** ******* *** ******* *** *** ********* *** **** **** and, correspondingly, using long sequences makes recognition more stable to small inter-step changes in walking style.
***** ******** ** ***** or ****** **** *********** such ** ***** ** a ****, ******** * backpack, ***. **** **** some ****** *** *** much ******* ** *** algorithm/ ************** ** *** gait ***********.********* ***** ******** ******** at ******** **** *** only ********* ********* ******* of *** **** ***** of ******** **** *********** uses:
**** ** * *********** of **** ***** *** movement. ** *** *** rocks ** **** *****, we ***** **** ** use *** **** ************ only, ** ******* **** movement ***** ******.
Gait *********** *********, **** ******** ******
**** *********** ** **** more *************** ********* **** alternatives ******* ** ******** multiple ******.**** *********** ** **** computationally **** **** *********** because ** ****** ** sequences ** ****** ******* of * ****** **** image. ********* ***** **** APNews **** *********** ******** more ******** ********* **** other **********:
**’* **** ******* **** other **********, ***************, ... It ***** ****** ********* to ** **** ******* you **** * ******** of ****** ****** **** a ****** *****.
**** ***** **** **** recognition **** **** ********* than ************. *** ********** grows ************* **** ******** images *** ******** *** gait *********** ******** ~* full ****** ** ******. The ******** ****** ** have **** **** *** 25.
Gait ********** ****, **** *******
*******, ********** ** *** easy *** ******** *** relatively *****.** *************** * ******* ******* and ******* *** ******** when ******** ******* *** used ** ******* ********* angles (******* ** *** right). **** ** **** time, *****, *** ********-********* than ********* * ****** high-quality **** *****.
**** *********** **** ** much **** ********** ******* governments **** ****** ** their ***********' **** ******, driver’s ********, *********, ****** data. *** ** *** United ******,** ** ***** ** scrape*** ** *** ******** of ****** ** ****** media *** *** ********.*** ******* **** *******,**-*****, ******** **,*** ******'* silhouette's **** ** ****** and ******* ** ** for ******** ******** ****. It ** **** ********** data ******* ** *****, which ***** ******* ****** cannot *** **.**** **** ** **** could **** ********* **** being **** ** ******* algorithms, ***** *** ********** of **********, *** **** algorithms **** ****** *** more ******. **-*****'* *******'* found *** ******** ** the **** ******** **** of ********* **** ****** go **** ~**% **** trained ** *** ****** to ~**% **** **,***. If *** ***** *********, we ***** ****** ~**% accuracy **** * ***,*** person *******
Appearance **. *****-*****
***** *** *** **** classes ** **********, **********, *** *****-*****. Appearance-based ****** **** ******* into * ********** *** use **** *** ********. Appearance-based *** *** **** common ******* **** *** simple, ****, *** **** computationally *********.**** ******* **********-*********** ******* ******** ****** into *** ***, **** Energy *****, ***** ****** are ********* **** *********** and ***** *********** *** averaged ******** ******** ******* shades ** **** ***** movement ******.
*****-***** ********** *** ****** of *** ***** **** and ***** ** * person ** ****** * model **** ********** **** instead ** * **********. They *** **** *********** and *************** ********* ******* they ******* **** *****. They *** **** ********* to ***** *** ***** variables ******* *** ****** they ****** *** **** affected ** ******* *********. A ******** ** **** can ****** * ********** in ** **********-***** ********* but * *****-***** ********* should ** **** ********. Changing *** ****** ***** has * ***** ****** on ***********,*** ****** ****** ** the **** ********** ** the ****** ** *** footage **** *** ***** from.
3D ******
***** **** **** **** the **** ********* *** complex ********** *** **. He **** ** ******** multiple ******* ** ****** a ** ***** ** the ******. **** *** be *********** ******* ** requires **** *********** ***-***, to ****** *** *** of ******** ** ******* by ******** *******, *** combining **** **** ******** cameras ** **** **** computationally ********* **** ***** a ****** ******.**********- ** *****-*************** *** ** **.
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Comments (16)
John Honovich
****, ***** ****!
*******, ** *** **** any *************** ** ******* edge ** ******** ************ you ***** **** **** to *******, ****** *** us ****.
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Skip Cusack
****, ****** **** ***. The ******** ** **** modality ** ****** ** accuracy. *'* **** ** see **** **** ******* to ********* ***** *********** in * ********** *** meaningful ***. **** * vendor ***** *% ********, what **** **** ****? As *** *** ********** one **** ***** ***** Positive *** ***** ******************* ******* ******** **** for ************** ****. *'* reluctant ** ****** ******* much ** **** * vendor **** ***** ***** own **********, ********** **** it's ********* ** ********. But *'** ******* ****!
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Undisclosed #1
**** **** * *** representing * ****-*** ******* to **********, (****-****), **** is *** ******** **** multiple *-****** ******** **** clamoring ***.
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Lynn Harold
* ****** ** ** could ******* **** -
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Charles Fletcher
* ****** **** ******* academic ****** ******** **** recognition ******** ***** ** either ***** ** *****-**** based *******. *****-**** ***** based ** *********** ******** of *** ******** ** an *********** ****** **** doesn't ******* ** *** it ** * ***** frame. *** **** ********** based ******** * ***-******** of *****-**** ***** *******. To ** ****, ** looks **** *** *** Gate ******/******* ** *** own ********. ** **, I **** ** ***** that **** ****** ******* like *** **** **** like * **** ** Silhouette **************. ** *** its *****-**** ***** **** three **********: ******* ****, Contour, *** **********, *** maybe *******.
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Undisclosed #3
**'* * ***** **** FST ********** ******'* **** on. **** *** * great ******* **** **** gait *********** ** *** method ** **************, *** it *** * *** ahead ** **'* **** I *****.
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Dwayne Cooney
**** *********** *******.
******** * ***’* *** this ** * ******* means ** ***********, ** seems ****** *** ****-***** forensics ** ********* ******* the *** ** *** article.
**** *** ****!
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Zach Segal
******:****** *** ********* **** ****** *****'* record ** *** *****-*. They ***** **.*% ******** on * ***** *******, 95.8% ** ******** **** bags, *** **.*% ** subjects ** *****. * have ***** **** *********** to *** *******.
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Zach Segal
******: ***************** ******* **** **** **** recognition ******** ** ***** 70 ***** ** ****** and **** ** **** 100 ***** **** ****** for *** ********. **** would **** * ~**-**** FoV *** * ***** camera.
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