Gait Recognition Examined
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 *******
**** *********** **** * ******'* ****** body *** ********* ** ******** **** but ** ** ** ** **** limited *** ***** ** ** **** to ********* *** ****** ** ** or **** **.
******* *** ** ** *** ** accurate ** **** *********** *** ***** supplement **** *********** *** ***** ********** because ** *** ********* **********. **** also *** ** *** ** **** with * ***** *** **** **** recognition, ***** ** ****** **** *****, and ** **** ** *****. ***,** ***** **** *** ******* ******* gait *********** (*****'*******) ****** *** ******* ** **** *********** reflecting *** ********** ********* ***** ********, significantly ****** ********* *****, ****** ******* issues, *** ********* **** **********.
What ** **** ***********
**** *********** **** *******'* ******* ******* ** ******** ** ************ them **** *** ******** ****** ***** (left *** ***** *****, ~* ****** of *******) **** ********* ******** ***** PPF ************ **** ***** **********. ****, though, ** * ******** ***** **** technology ****** ** **** **** *** someone ***** (*.*., ***** '****'). *** ***** ** **** ****, ***********, posture, *** **** *** ******** **** alongside **** ********* *** ******* ***** factors.******* *** **** ************** ** **** **** ***** *** than **** *********** ******* **** *********** relies ** ****** ********.
High ***** **** ********
****** **** ***********, **** *********** ******** a ******** ** ******. ************* *****, *** ********** **** ***********, **** IPVM **** ************ **** *****/****** ** ******* * second **** ****** *** ******* ** higher ******** *** **** ****** **********. This ***** **** *********** ** *** more *************** ********* **** ***** ********** because ** *** **** ****** ****** of ****** ****** ** ********** **.*** ***** ******* ****** **** *** *** *** achieved ** ******** ** **% **** 50 ******, **% **** **, *** 99% **** ** *** ********* *** test **** *** *** *** ********* so *** ******** *** *** ********** of *******. **** **** ********* **** gait ****** ******** **** **** ****:
******** *** ****** ** *** **** cycle ** ***** * *, **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.
***** **** **** "**** ***** ******* to ** ******" **** ************ ********* reaching **,*** ****** ******* **********.
Gait **** ******** **** **** ***********
**** *********** ** ***** ******** ** **** ***********. **** recognition ******** *****************%-**% ****** ********* ***** ****.**+% *** *** *** **** ********************* ** **** ******* *** ***** results *** ** ********** ******* **** are ***** ** ********** *********.
** ********,* ********** ** ***** ************ * **** ********* **** ********* an ******** ** **** **-**%. * key ********** ** **** *** ******* were ****** ** **.***/** ******* ** chest *****, ** **** *** ** most **** *****, *** ******* ***** shows *** ***** ******** *****:
**** ******* * **,*** ****** **** set *** **** *** ****** ** 25 ***, ~** ******, ** ****** 2-D **** *** ******** ****** ****** to ******** ****-**** ***** *** ***** of ******* **** *** ****** ***** up. **** ****** * ****** **********-***** models (**** **** **** ** **********-*****) and ***** * *** ***** ********** had ********** ** **-**% *** * other ********** ******* * *** **%. Even ** *** ***** ********** ********* was ******* *** ******** ******** **%, the **** ********** ***** **** **** than **% ******** ** *** **** realistic ****.
Factors ********* ********: ****** *****, ********, *****, ******* ******
********, *****, ******* *******, *** **** importantly ****** ***** ****** **** *********** accuracy. ****** ***** ******* *********** ******* people **** ********* **** ********* *****.*** **** ** *************** ****** ***** ** *** **** difficult *** ********* ****** ********* **** recognition:
****** *****, *******, *** ********** *** most *********** ******— ***** *** ****** the **** ******** ** * ****** manner
****** **** ************* ********* *****, **** ***** ** their *****, ***** *******, *** ***** carrying *******, ** **** ******* *** accuracy ** **** ***********.**** *********** *** ** ********** ******** *** ******** ******** *******. Clothes *** ******* ****** * ********** and *** **** ****** * ******’* walk.
~20 *** ********
**** *********** *** **** **** **** 100 ***** **** ****** ** in **** *****. ***************** ******* **** ****** ***** **** *** ***** ****** people, ** ** *** *** ******* who ** * **** **** *** 16.7 *** *** ******* *** ** 6 **** ****:
*** ****** ** ****** ** * reasonable ******** ********** *** **** ***********. With ****** **********, *** **** ***** observe ****** ***********.
**** ***** **** * ~**-**** *** for * ***** ******. ** **** stated **** ** **-** ****** ** height, **** *********** ******* **** ***********. This **~*/*** ** **** **** ****** ******** *** **** ********* **** recognition.
Gait *********** **** ** ***** *****
**** ************* *** ******** ** ******* *** type ** **** ***** ** **** not ****** ** ***'* ****, ***** is ** ********* ******** ** ****** recognition.
***** ******** ** ***** ** ****** gait *********** **** ** ***** ** a ****, ******** * ********, ***. will **** **** ****** *** *** much ******* ** *** *********/ ************** of *** **** ***********.********* ***** ******** ******** ** ******** gait *** **** ********* ********* ******* of *** **** ***** ** ******** gait *********** ****:
**** ** * *********** ** **** shape *** ********. ** *** *** rocks ** **** *****, ** ***** have ** *** *** **** ************ only, ** ******* **** ******** ***** change.
Gait *********** *********, **** ******** ******
**** *********** ** **** **** *************** intensive **** ************ ******* ** ******** multiple ******.**** *********** ** **** *************** **** face *********** ******* ** ****** ** sequences ** ****** ******* ** * single **** *****. ********* ***** **** APNews **** *********** ******** **** ******** computers **** ***** **********:
**’* **** ******* **** ***** **********, computationally, ... ** ***** ****** ********* to ** **** ******* *** **** a ******** ** ****** ****** **** a ****** *****.
**** ***** **** **** *********** **** more ********* **** ************. *** ********** grows ************* **** ******** ****** *** combined *** **** *********** ******** ~* full ****** ** ******. *** ******** number ** **** **** **** *** 25.
Gait ********** ****, **** *******
*******, ********** ** *** **** *** datasets *** ********** *****.** *************** * ******* ******* *** ******* are ******** **** ******** ******* *** used ** ******* ********* ****** (******* to *** *****). **** ** **** time, *****, *** ********-********* **** ********* a ****** ****-******* **** *****.
**** *********** **** ** **** **** accessible ******* *********** **** ****** ** their ***********' **** ******, ******’* ********, passports, ****** ****. *** ** *** United ******,** ** ***** ** ********* ** *** ******** ** ****** on ****** ***** *** *** ********.*** ******* **** *******,**-*****, ******** **,*** ******'* **********'* **** 14 ****** *** ******* ** ** for ******** ******** ****. ** ** also ********** **** ******* ** *****, which ***** ******* ****** ****** *** it.**** **** ** **** ***** **** companies **** ***** **** ** ******* algorithms, ***** *** ********** ** **********, and **** ********** **** ****** *** more ******. **-*****'* *******'* ***** *** accuracy ** *** **** ******** **** of ********* **** ****** ** **** ~35% **** ******* ** *** ****** to ~**% **** **,***. ** *** trend *********, ** ***** ****** ~**% accuracy **** * ***,*** ****** *******
Appearance **. *****-*****
***** *** *** **** ******* ** algorithms, **********, *** *****-*****. **********-***** ****** turn ******* **** * ********** *** use **** *** ********. **********-***** *** the **** ****** ******* **** *** simple, ****, *** **** *************** *********.**** ******* **********-*********** ******* ******** ****** **** *** GEI, **** ****** *****, ***** ****** are ********* **** *********** *** ***** silhouettes *** ******** ******** ******** ******* shades ** **** ***** ******** ******.
*****-***** ********** *** ****** ** *** human **** *** ***** ** * person ** ****** * ***** **** represents **** ******* ** * **********. They *** **** *********** *** *************** intensive ******* **** ******* **** *****. They *** **** ********* ** ***** and ***** ********* ******* *** ****** they ****** *** **** ******** ** outside *********. * ******** ** **** can ****** * ********** ** ** appearance-based ********* *** * *****-***** ********* should ** **** ********. ******** *** camera ***** *** * ***** ****** on ***********,*** ****** ****** ** *** **** regardless ** *** ****** ** *** footage **** *** ***** ****.
3D ******
***** **** **** **** *** **** resilient *** ******* ********** *** **. He **** ** ******** ******** ******* to ****** * ** ***** ** the ******. **** *** ** *********** because ** ******** **** *********** ***-***, to ****** *** *** ** ******** is ******* ** ******** *******, *** combining **** **** ******** ******* ** much **** *************** ********* **** ***** a ****** ******.**********- ** *****-*************** *** ** **.
Simulating ******
*** **** *** ************* **** ******** ** ******** **** other ****** ****** ***** **** ************ mitigates *** ****** ** ********* ****** angles ***** *** ********* ******** *******, which **** *** ** ***********:
***** ******* ******* * ***** ********** and *********** ******** ****** ************, ***** limits *** *********** ** **** ***** surveillance *********.
**** **** **** ** ***** *-* silhouettes **** ********* ****** **** * ~30 ********** ***** ***** ** ********, compared ** ***** ********** **** *** 60+ ********** ***** ***** **** ******** images ***** **** ********* ******.
One *** *******, ****** ** ******* **** ***********
******** * ******* ******** ****** ******* that*** ********* **** ************************ ** *** ****** ** *** People's ********** ***** *** ** **** ** the***** ************ **************. ** **** *** ***** ****** else ******* *** **********. ****** ****** their ******** ** **% ******** *** works ** ***/***** *** *** *** specify *** ** *** ************. ****** says **** ********* ** ********* ********* to *********** ******** *** **** *** require ********* ********.
****** *** ***** ******** ****** **** *** *********’* **** **** analysis ***** ** ********* ** ********:
******** **** **** ***** ****** *** recognition ***** *** ** ******* *** of * ******’* ****
****** **** *** ************ ***** **** ** * ************ use **** *** ****-**** *** ************* by *** ******* ****** *** ********** for ** *****, ********** **** * TB ** ****** *******.** ******* ****** **** ** **** they**** ** ************ ** **** ~$** million ** ********** ** ********* ****** China.**** ***** ******** ******:
** *** ********* ******* **** ****** on ******** **************, **** ** ******** suspects **** * ******* *****, ... Currently, ***** *** ***** ***,*** ****** criminals ** *** ***** *** ********. [Our ********’*] ******** ******** ***** **** a ***** **** ******.
**** ********* ***** ***** ****** ******* more ** *** ********** *** *** use ** *** ***.
********,****** ********* ********* ********************/**** ******** ***** ** **** ******* tracing.
** ******** ** ***** ******* *** in *****, ** *****/* ** ** data, ****** **** *** ***** ******* outside ** ***** ** ******* ***** 2018. ***** *** *** ****** ***** for ****** *** **** ***********. ** would ****** ** *** **** ***** competition *** **** ****** ******* ****** if ****** *** **** ** ******* 96% ******** ** ****-***** *****.
Dahua ****** ** ***** ****** ** **** *********** ****; **** ****** ******
** ****** ****,***** ********* **** ****** *** ****** ** the*****-* **** *********** *******.*** **** *********** ** ** ****** ** *** subjects ******* **** ******** ******. ** is *********** ******* ** *** ***** backgrounds, * **** ******** ** ***** angles, *** ** **** ** * low ****** (** ****-****). ****-***** ******** could ** **** ********* **** *****-* accuracy. *** ***** ****** **** **** makes ****** ***** ** ************* ****** ** ********* **** ***** very **** *** **** *** ****** in *** *******.** *** ***** ********,
**** **** ********* ... ******* *** person *** ***** ******* *** ************ between *******. **** ** ** ****** a ***** ********, ** ** **** life ****** ***** **** ******** **** their ****** ************; ************, **** ********* allows *** *** ******** ** * model *** ******** ** *** ******* of **** *********** *** ** ************* solved.
***** ********.*% ******** ** * ***** *******, 94% ** ******** **** ****, *** 87% ** ******** ** *****. ** have *** ***** *** ********** ** Dahua ******* **** ***********.
******:****** *** ********* **** ****** *****'* ****** ** the *****-*. **** ***** **.*% ******** on * ***** *******, **.*% ** subjects **** ****, *** **.*% ** subjects ** *****.
Examples ** *** ** ****** ******* ** *****
**** *********** *** **** **** ** evidence ** ****** ** ***** *** Denmark
- ***** ***** **** *** ******** ****** have**** **** *********** ***** **** *** famously ****** * ******* ** **** by **** *********** ¾ ****** *******. They *** ** *** **** ** times ******* ********* *** *** ****.
- **** *********** *** **** ** ********* evidence ** * ******* ** ******* in ****. ******* ***** ****** *** crime *** ******** **** ******** ******* of *** ******* ** ******** *** was *** **** *** *** *************.
American ******* ** ******** ******* ***** *******
** ******* ***** ******* ** ******** ********(*** ******** *********** ** *** ******** Academy ** ******** ********)***** ******* **** ***** **** ************* ******** ** ****.
*********, ****** ****** ***** **** ******** with *******, ** **** ****** *** other **** ** ******** *********** **** disciplines ******* ***** *********** ***** ** practice, ***** *****, *** ************ ************ in ******** *********.
Gait *********** *** ************/****** *******
********* *** **** *********** *** ** used *** ******* ****** *******/************ ******* a *******’***** *****,***** *******, *********. ****** **** ***********, **************, ** manual ************ **** ******* *** ** done *********/************* ******* ************.**** ** * *** **************** ** ******* *** ************* ** security ***** ****** ****** ** ***-*** of *********, **** ******, ** **** recognition *** ****** *** ***** ********. Users ***** **** **** * **** that ***** ************* *** ****. ** addition, ****** ***’* **** ** ***** about **** ***** ******** ***** *****, looking **** ** ***** ******, ** wearing ******* **** ******** * *** feature.
UnifyID *** **********-***** ************** ********
********* * ********** ******* **** ** focused ** ******** ************** ******* ********** including **** ***********. **** **** ******** mobile *****-***** **** ************************** ******* **** ** ********** **** access *******, **-***-*********, ******* ******, *** other ****.******* **** **** **** ******** ***** on ******** *** ******* **** **************, including *****-*** ****** ******* *********** ** face *********** ************. ******* **** **** that *** ********* ** ****** ** the ****'* ***** *** ****** ***** behavior. ** ***** ** ****** *** by ***** ****** ** * ********* pocket, ******* ********* *****, *** ***** factors **** ****** **** ***********, *** UnifyID **** *** ********* *** ***** that ***** ******* *** ***** ***. It ***** ** ****** ** ********* you **** ***** ********* *******, *** needs ** ***** *** **** **** change.
**** **** **** *** ********** ** c********* **** ************, *** ** **** not ****** **:
*** ******** **** ******** ** ** par **** ******** ************ ***********, **** a */**,*** ***** ******** ****.
Other ********** - ***** *****, ***** *******
*********** **** **** ******** ***** ***** sensors, ***** ***** ** *********, ******* of *******. **** ***** ******* ******** and **** **** *** ********** ********* preventing ******* ********.* **** ** **** *********** ***** ***** *****, ***** ***** be **** **** ***** *** **** cameras *** **** ** *** ******** scenario.
Gait ******** *** ********** ********, ******* *******, ******** ****
**** *** **** ** **** ** watch ****** *******, *** ********** ********, and **** ***** ********** *******.****** **** ***** *************** *********** ** **** ** **** people *** **** ****, **** ** elderly *********** *** **** ****** ****. Cameras ***** ** **** ** **** for ****** *** **** ****** *** then ** *** *** **. **** recognition ****** **************** ******* ***** **** *********** ****** **** ***** ******* ***** ** ****** **** ******** to ******* *** *****:
** *** ******, * **** **** to ***** *** ************ ** ***** health ** ****** **** *** **** they ****
** ***** **** ** **** ** look *** ********** ********.* **** **** ******* **** ********* a *********** ** ***** ***** **** *** analyze ********.**** **** *************** ***** *************** ** ** *** **** **** because **'* ***** ** ***** ***** and ** *********** *** *** **** reason. **** **** ** ****** ****** specific *********** *** *** **** ** someone ** ******** ************ *** ****** information ***** ***** ****, ** ** fast, ****, **********, ***. ***** **** principles ***** ** ******* ** ********* pedestrian ********. *** ***** *** **** areas **** **** ******* *** *****/**** pedestrians **** *** **** ** *********** or *****.** **** *** ** **** ********* *********** ** ******, ***, *** physical *************** ** *********** ***** ***** less ********, *** **** *************** *********** than **** ***********.
*******
**** *********** *** **** ** ********** where **** *********** *** ***** ********** don’t ** ** ***** **** ********** surveillance *******. ** *** **** **** access *******/************** * ****** *** **** private **** ********** **** ** ********* to **** *********. *******, *** **** of ********* ******* ** *** ******* gait *********** ** * **** *** technology *** ***** *****. ******* ***** stem **** **** ***********'* ***** ************* demand, ***** ********, *** ****** ** factors **** ** ***** ** ****, the ********** ** ********** ****, ** lack ** ********, ** ** * bad ****.
****, ****** **** ***. *** ******** of **** ******** ** ****** ** accuracy. *'* **** ** *** **** hold ******* ** ********* ***** *********** in * ********** *** ********** ***. When * ****** ***** *% ********, what **** **** ****? ** *** all ********** *** **** ***** ***** Positive *** ***** ******************* ******* ******** **** *** ************** mode. *'* ********* ** ****** ******* much ** **** * ****** **** about ***** *** **********, ********** **** it's ********* ** ********. *** *'** believe ****!
***** *** ****. *** * *****. Accuracy *** ************** ** **** *********** to ******** ******* ** ********* *** the ******* *** **** *********.
**** **** * *** ************ * face-rec ******* ** **********, (****-****), **** is *** ******** **** ******** *-****** agencies **** ********* ***.
* ****** ** ** ***** ******* this -
***** ** **** *** ********** *** Monty ******Ministry ** ***** ***** before I did.
***, **** ********* ** **** * feasible ******** ** ***** **** *** being ******** ****'* ***** ** *** analytic. ********* **** *** *****, ** ** *** ** *** easiest ********* ** ****** ***** ** simply ********* *** *** ***** ********* walk ******.
* ****** **** ******* ******** ****** classify **** *********** ******** ***** ** either ***** ** *****-**** ***** *******. Model-free ***** ***** ** *********** ******** of *** ******** ** ** *********** object **** *****'* ******* ** *** it ** * ***** *****. *** call ********** ***** ******** * ***-******** of *****-**** ***** *******. ** ** fair, ** ***** **** *** *** Gate ******/******* ** *** *** ********. To **, * **** ** ***** that **** ****** ******* **** *** seem **** **** * **** ** Silhouette **************. ** *** *** *****-**** falls **** ***** **********: ******* ****, Contour, *** **********, *** ***** *******.
**'* * ***** **** *** ********** couldn't **** **. **** *** * great ******* **** **** **** *********** as *** ****** ** **************, *** it *** * *** ***** ** it's **** * *****.
*** ** ****** * ***** *******? Did ** ****** *** **** ***********? It *** * ***** ******* *** what *** *** ******* ** **** actually ***** **** ***********.
*** ******* ****'* *******, *** ** worked *** ****** ************ **** ***** (very, **** *********) ************ **********/************ **** followed. *** ***, **** *** **** gait *********** ***** **** ***** *******.
*** ******* **** *** ****'* *** performance ** *** *******, ** *** the ********* ************ **** *** *** it ** ****. **** ***** ** the **** ** ****** ********, ***** meant **** ** ****** ****'* **** the ************ **** *** ********, ** wouldn't ***** ******. *** **** *** right ********, ** *** ********** ********** with ** ********* ***** ****** ** failures.
* **** ***** **** **** **** a *** *** ***** ** ***** of *** ********** ***** ******** ** make *** ****** **** ** ****. It ****** ****** *** ** *** that *** ****** * ****** *** every * ****** ** *******. ** reality, **'* ****** ******* ** **** of *** ********* ********* ***** ****, but ~* ***** ***, **** ****'* nearly ** ******.
**** ********!
****, **** *********) ************ **********/************...***** ** *** ********** ***** ********
**'* **** ** *** ******** **** those ***********. **'* ********* **** **** and * **** ********** ***** **** topic, *** ********** ***** ********** **** but ** **'* *** ******* ** costly ** *** ** **********, ******** will ******.
**** *********** *******.
******** * ***’* *** **** ** a ******* ***** ** ***********, ** seems ****** *** ****-***** ********* ** described ******* *** *** ** *** article.
**** *** ****!
******:****** *** ********* **** ****** *****'* ****** ** the *****-*. **** ***** **.*% ******** on * ***** *******, **.*% ** subjects **** ****, *** **.*% ** subjects ** *****. * **** ***** this *********** ** *** *******.
******: ***************** ******* **** **** **** *********** ******** at ***** ** ***** ** ****** and **** ** **** *** ***** high ****** *** *** ********. **** would **** * ~**-**** *** *** a ***** ******.
****, ***** ****!
*******, ** *** **** *** *************** on ******* **** ** ******** ************ you ***** **** **** ** *******, please *** ** ****.