Gait Recognition Examined

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

What ** **** ***********

**** *********** **** *******'* walk*** ******* ** ******** or ************ **** **** one ******** ****** ***** (left *** ***** *****, ~1 ****** ** *******) with ********* ******** ***** PPF ************ **** ***** biometrics. ****, ******, ** a ******** ***** **** technology ****** ** **** than *** ******* ***** (i.e., ***** '****'). *** ***** ** **** body, ***********, *******, *** arms *** ******** **** alongside **** ********* *** walking ***** *******.******* *** **** ************** ** **** **** fewer *** **** **** recognition ******* **** *********** relies ** ****** ********.

High ***** **** ********

****** **** ***********, **** recognition ******** * ******** of ******. ************* *****, *** ********** **** recognition, **** **** **** recognition* **** *****/****** ** roughly * ****** **** higher *** ******* ** higher ******** *** **** higher **********. **** ***** gate *********** ** *** more *************** ********* **** other ********** ******* ** the **** ****** ****** of ****** ****** ** accomplish **.*** ***** ******* ****** **** *** few *** ******** ** accuracy ** **% **** 50 ******, **% **** 60, *** **% **** 70 *** ********* *** test **** *** *** not ********* ** *** accuracy *** *** ********** of *******. **** **** mentioned **** **** ****** slightly **** **** ****:

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

***** **** **** "**** still ******* ** ** unique" **** ************ ********* reaching **,*** ****** ******* duplicates.

Gait **** ******** **** **** ***********

**** *********** ** ***** ******** ** **** recognition. **** *********** ******** claims***********%-**% ****** ********* ***** vs**.**+% *** *** *** face ********************* ** **** ******* but ***** ******* *** be ********** ******* **** are ***** ** ********** scenarios.

** ********,* ********** ** ***** team******** * **** ********* test ********* ** ******** of **** **-**%. * key ********** ** **** the ******* **** ****** at **.***/** ******* ** chest *****, ** **** are ** **** **** tests, *** ******* ***** shows *** ***** ******** angle:

**** ******* * **,*** person **** *** *** used *** ****** ** 25 ***, ~** ******, to ****** *-* **** and ******** ****** ****** to ******** ****-**** ***** the ***** ** ******* does *** ****** ***** up. **** ****** * common **********-***** ****** (**** only **** ** **********-*****) and ***** * *** based ********** *** ********** of **-**% *** * other ********** ******* * and **%. **** ** the ***** ********** ********* was ******* *** ******** accuracy **%, *** **** algorithms ***** **** **** than **% ******** ** the **** ********* ****.

Factors ********* ********: ****** *****, ********, *****, ******* ******

********, *****, ******* *******, and **** *********** ****** angle ****** **** *********** accuracy. ****** ***** ******* recognition ******* ****** **** different **** ********* *****.*** **** ** *************** ****** ***** ** the **** ********* *** important ****** ********* **** recognition:

****** *****, *******, *** considered *** **** *********** factor— ***** *** ****** the **** ******** ** a ****** ******

****** **** ************* ********* *****, **** rocks ** ***** *****, while *******, *** ***** carrying *******, ** **** impacts *** ******** ** gait ***********.**** *********** *** ** affected** ******** *** ******** carrying *******. ******* *** objects ****** * ********** and *** **** ****** a ******’* ****.

~20 *** ********

**** *********** *** **** done **** *** ***** high ****** ** in **** *****. ***************** ******* **** ****** ***** **** *** Pixel ****** ******, ** ** *** for ******* *** ** 5 **** **** *** 16.7 *** *** ******* who ** * **** tall:

*** ****** ** ****** is * ********** ******** resolution *** **** ***********. With ****** **********, *** will ***** ******* ****** performance.

**** ***** **** * ~60-foot *** *** * 1080p ******. ** **** stated **** ** **-** pixels ** ******, **** recognition ******* **** ***********. This **~*/*** ** **** **** says** ******** *** **** condition **** ***********.

Gait *********** **** ** ***** *****

**** ************* *** ******** ** wearing *** **** ** mask ***** ** **** not ****** ** ***'* face, ***** ** ** advantage ******** ** ****** recognition.

***** ******** ** ***** 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 ****** ******.**********- ** *****-*************** *** ** **.

Simulating ******

*** **** *** ************* **** ******** ** simulate **** ***** ****** angles ***** **** ************ mitigates *** ****** ** different ****** ****** ***** not ********* ******** *******, which **** *** ** unrealistic:

***** ******* ******* * fully ********** *** *********** multiple ****** ************, ***** limits *** *********** ** real ***** ************ *********.

**** **** **** ** match *-* *********** **** different ****** **** * ~30 ********** ***** ***** in ********, ******** ** other ********** **** *** 60+ ********** ***** ***** when ******** ****** ***** from ********* ******.

One *** *******, ****** ** ******* **** ***********

******** * ******* ******** vision ******* ******* ********* **** ************************ ** *** ****** in *** ******'* ********** ***** *** ** part ** ******** ************ **************. ** **** *** found ****** **** ******* the **********. ****** ****** their ******** ** **% accurate *** ***** ** 50m/165ft *** *** *** specify *** ** *** requirements. ****** **** **** detection ** ********* ********* to *********** ******** *** does *** ******* ********* subjects.

****** *** ***** ******** told** **** *** *********’* full **** ******** ***** it ********* ** ********:

******** **** **** ***** reduce *** *********** ***** but ** ******* *** of * ******’* ****

****** **** *** ************ ***** **** ** a ************ *** **** for ****-**** *** ************* by *** ******* ****** and ********** *** ** cases, ********** **** * TB ** ****** *******.** ******* ****** **** in **** ******** ** ************ ** sell ~$** ******* ** technology ** ********* ****** China.**** ***** ******** ******:

** *** ********* ******* with ****** ** ******** investigations, **** ** ******** suspects **** * ******* scene, ... *********, ***** has ***** ***,*** ****** criminals ** *** ***** and ********. [*** ********’*] database ******** ***** **** a ***** **** ******.

**** ********* ***** ***** Watrix ******* **** ** its ********** *** *** use ** *** ***.

********,****** ********* ********* ********************/**** ******** ***** ** ease ******* *******.

** ******** ** ***** limited *** ** *****, 20 *****/* ** ** data, ****** **** *** claim ******* ******* ** China ** ******* ***** 2018. ***** *** *** strong ***** *** ****** and **** ***********. ** would ****** ** *** more ***** *********** *** more ****** ******* ****** if ****** *** **** to ******* **% ******** in ****-***** *****.

Dahua ****** ** ***** ****** ** **** *********** ****; **** ****** ******

** ****** ****,***** ********* **** ****** *** record ** ********-* **** *********** *******.*** **** *********** ** ** ****** of *** ******** ******* from ******** ******. ** is *********** ******* ** has ***** ***********, * full ******** ** ***** angles, *** ** **** at * *** ****** (no ****-****). ****-***** ******** could ** **** ********* than *****-* ********. *** small ****** **** **** makes ****** ***** ** ************* ****** ** ********* that ***** **** **** for **** *** ****** in *** *******.** *** ***** ********,

**** **** ********* ... contain *** ****** *** video ******* *** ************ between *******. **** ** of ****** * ***** approach, ** ** **** life ****** ***** **** together **** ***** ****** intersecting; ************, **** ********* allows *** *** ******** of * ***** *** checking ** *** ******* of **** *********** *** be ************* ******.

***** ********.*% ******** ** * plain *******, **% ** subjects **** ****, *** 87% ** ******** ** coats. ** **** *** found *** ********** ** Dahua ******* **** ***********.

******:****** *** ********* **** ****** *****'* record ** *** *****-*. They ***** **.*% ******** on * ***** *******, 95.8% ** ******** **** bags, *** **.*% ** subjects ** *****.

Examples ** *** ** ****** ******* ** *****

**** *********** *** **** used ** ******** ** courts ** ***** *** Denmark

• ***** ***** **** *** Japanese ****** ******** **** *********** ***** 2013 *** ******** ****** a ******* ** **** by **** *********** ¾ masked *******. **** *** it *** **** ** times ******* ********* *** May ****.
• **** *********** *** **** as ********* ******** ** a ******* ** ******* in ****. ******* ***** during *** ***** *** compared **** ******** ******* of *** ******* ** evidence *** *** *** used *** *** *************.

American ******* ** ******** ******* ***** *******

** ******* ***** ******* ** ******** Sciences(*** ******** *********** ** the ******** ******* ** Forensic ********)***** ******* **** ***** gait ************* ******** ** ****.

*********, ****** ****** ***** gait ******** **** *******, as **** ****** *** other **** ** ******** originating **** *********** ******* fully *********** ***** ** practice, ***** *****, *** demonstrable ************ ** ******** scenarios.

Gait *********** *** ************/****** *******

********* *** **** *********** can ** **** *** passive ****** *******/************ ******* a *******’***** *****,***** *******, *********. ****** **** ***********, fingerprinting, ** ****** ************ gait ******* *** ** done *********/************* ******* ************.**** ** * *** advantage******* ** ******* *** inconvenience ** ******** ***** causes ****** ** ***-*** of *********, **** ******, or **** *********** *** phones *** ***** ********. Users ***** **** **** a **** **** ***** automatically *** ****. ** addition, ****** ***’* **** to ***** ***** **** masks ******** ***** *****, looking **** ** ***** phones, ** ******* ******* that ******** * *** feature.

UnifyID *** **********-***** ************** ********

********* * ********** ******* that ** ******* ** changing ************** ******* ********** including **** ***********. **** have ******** ****** *****-***** gait ************************** ******* **** ** integrated **** ****** *******, in-app-purchases, ******* ******, *** other ****.******* **** **** **** GaitAuth ***** ** ******** and ******* **** **************, including *****-*** ****** ******* fingerprint ** **** *********** capabilities. ******* **** **** that *** ********* ** stored ** *** ****'* phone *** ****** ***** behavior. ** ***** ** thrown *** ** ***** placed ** * ********* pocket, ******* ********* *****, and ***** ******* **** affect **** ***********, *** UnifyID **** *** ********* can ***** **** ***** changes *** ***** ***. It ***** ** ****** to ********* *** **** these ********* *******, *** needs ** ***** *** with **** ******.

**** **** **** *** technology ** ********** **** ************, *** we **** *** ****** it:

*** ******** **** ******** is ** *** **** physical ************ ***********, **** a */**,*** ***** ******** rate.

Other ********** - ***** *****, ***** *******

*********** **** **** ******** using ***** *******, ***** ***** ** invisible, ******* ** *******. This ***** ******* ******** and **** **** *** technology ********* ********** ******* backlash.* **** ** **** looking**** ***** ***** *****, which ***** ** **** with ***** *** **** cameras *** **** ** any ******** ********.

Gait ******** *** ********** ********, ******* *******, ******** ****

**** *** **** ** used ** ***** ****** falling, *** ********** ********, and **** ***** ********** traffic.****** **** ***** *************** *********** ** **** to **** ****** *** need ****, **** ** elderly *********** *** **** fallen ****. ******* ***** be **** ** **** for ****** *** **** fallen *** **** ** not *** **. **** recognition ****** **************** ******* ***** **** *********** ****** **** ***** Times** ***** ** ****** from ******** ** ******* use *****:

** *** ******, * also **** ** ***** the ************ ** ***** health ** ****** **** the **** **** ****

** ***** **** ** used ** **** *** suspicious ********.* **** **** ******* Tech ********* * *********** ** ***** ***** that *** ******* ********.**** **** *************** ***** technology***** ** ** *** feet **** ******* **'* based ** ***** ***** and ** *********** *** the **** ******. **** said ** ****** ****** specific *********** *** *** tell ** ******* ** behaving ************ *** ****** information ***** ***** ****, is ** ****, ****, deliberate, ***. ***** **** principles ***** ** ******* to ********* ********** ********. You ***** *** **** areas **** **** ******* and *****/**** *********** **** and **** ** *********** or *****.** **** *** ** able ********* *********** ** ******, age, *** ******** *************** of *********** ***** ***** less ********, *** **** computationally *********** **** **** recognition.

*******

**** *********** *** **** in ********** ***** **** recognition *** ***** ********** don’t ** ** ***** help ********** ************ *******. It *** **** **** access *******/************** * ****** and **** ******* **** experience **** ** ********* to **** *********. *******, the **** ** ********* working ** *** ******* gait *********** ** * sign *** ********** *** major *****. ******* ***** stem **** **** ***********'* large ************* ******, ***** accuracy, *** ****** ** factors **** ** ***** or ****, *** ********** of ********** ****, ** lack ** ********, ** is * *** ****.