Long Distance Biometric Recognition $12 Million Funding (PhD Interview)
Can you identify a person over 1,000 feet away just from a camera? Biometrics are typically done at very close range, but FarSight seeks to do this from very far away.
In this article, based on an interview with Professor Liu, a review of academic publications, and publicly available documents, we detail the improvements proposed by FarSight and its approach to multimodal biometric recognition.
For background on gait recognition, see Single Frame Gait Recognition From Michigan State and Osaka University Examined.
Executive *******
***** **** ** *** **************, *** they *** **** ** *********, **** technology ***** ******* *********** ****** *** from *******, ****** ********* ******** ******. Structurally, *** ********* ** **** **** can **** **** ******** ******* *** need ** ***** **** **** * single ******. **** ***** * **** positive **** ** ~**%, **** ***** positives *** ** * *% ****, with ***** ******* ** *****+. ****, they ******* **** ***** *********** ** improve ************** **** ***** *** ********. However, ******* ********* *********** ***** ************* reduce ****-***** ************, ********* ******** ********* of ******* **** *** **** *****, plus ******* **** ******* ******** *********** with ****-***** ************.
Developed ***** *** ***** *******
*****'********** *********** *** ************** ** ******** And ***** (*****) *********** ** ******* ***** ********* ** identify ****** ** **** ****** (**** 1000 ****) *** **** ******** ********* (drones, ***** ******, ***.). ******** (*** ******** *****) ** ****** ** *** ***** project ***$** *******.
*** ******** ****** ** ********* ***** the ***** *******. *** **** ** known *** *********** ****** ********, ******** from ************ ** *********. * *** years ***, *** ******* ************* (**) recognized *** **** *** ****** **************. This *** ** *** ***** *******, aiming ** ******** *********** **** ****, up ** ******* ******* ******.
*** ********* *** **** ** *** project ** ** ******* ********* *********** over **** ********* *** ** ** so, ******** ********* ***** ******: ****** detection, ***** ***********, *** ********** ********* recognition.
******** ********* ******* *** **********: ********* and ********, ***** *********** ** ***********, and ***** ********* ********** - ******, body *****, *** **** ***********, ******* to * ***** ********* ******. *** project's ********** **** ** ********** *****-******* images ** **** *********, ********* **** for ********* ********.
*** ******* ******** ******* **********, ********* capture ********* ** *,***', ~*,***', *** ~3,200', *** ***** ****** ** **** than ** *******. **** * ****** is ********, ***** ****, ****, *** body ***** *** ********, *** ********* recognition ** *********.
*** ****-***** ************ (>*,*** ****), *** project **** **** ****** ** ******* ~15 ****** *** ***** (~** ***) and **-*** ****** *** * ****** head-to-toe (~** ***), ********* ** ***.
** ****** ******, ** *** **** and **** ******, ******* ***** ** view *** ********. ** **** ****, we ******* ***** ** ****** ****** the **** *** **-*** ****** **** head ** ***. ***** *** *****, it's * ******** **********.
Image ***********
************* ******* **** ********** *** *********** interference ******** ****** ****-***** *******, ********** the ******. ******** **** * ********** modeling ********* ** ******* *** ******* images. *** ********* ********* *** ********** effects ** ********** ** *** ***** and ******** ***** ****** ***** * recurrent ******* *****.
***** ***** *********** *** ******* *** image *******, *** **** ****, **** method ***** ******* *********** *** ********* recognition. *** ***** *** ~*-*% ******** improvement ******** ** ***** *********** ** minimal *** ************* ****** *** ********* identity, ******* *** *******.
***** ***********, ************ ********** *** ***** recognition, ***** ******* *********** ** ********* performance ***** *** ***** *******. ** fact, **** ***** **** ********* ***** restoration, ******* ** ***** *****************. *** challenge **** ** *** ******** *********** in ****** ******. ****** **** ******* inadvertently ****** ***** ********, ************ ********* analysis. *****, * *-*% *********** ** what **'** ********, ***** ** *******.
Biometric ***********
*** ******** ********* ******** ***** ********* modalities: ****, ****, *** **** *****. The ****** *********** ********* **** *** in-house **************** *********; ***** *** ** ***** **********, facial *********** ** *** ***** ******** for ****-***** *******, ** ********' ***** might *** ** *******. *** **** IPVM **** ** *** ** *** training ****, ***** **** ****-******* **** images *** **** *** ******* **** similar ******** ** *****-******* (***** ***) images.
************* ***-******* **** ** ******** *** help. *******, ****** * ******* ***** of ***********, ******* *********** ******* **********. The *** ** *********** ********* *** recognition *********** ***** ** ***** *******.
*** ****** ********* ******** **** ****. The ********* **** * ******** ******* on *** ****** *******. ****, ** analyzes *** ****** ** ********* *** gait (********* ***** **** *********** **** ******** State *** ***** ********** ********). ***** **** ** *** **** reliable ********* ******** ** *** ***** for ****-******** ************, *** ****, ** can ** ********** ** ******* *******, including ******* ***** *** ******* *****.
**** ** ********** ** ******* *****, clothing, *** ******* *****, *** *****'* insufficient **** ** ********* *** *********** over *****. **** ***** ***********, **** nascent **** ****, ******* ** ******** people ** ********* ******* *** *****, presenting ****** **********.
*** ***** *** *** ****** ************************ ** ****-***** ***********. ****-***** ********** is ********* ** *** ************ ** person **-** *** ********* "***** ** body *****" ** ************** ** ****** and ******** ********.
** ***** **** *** **** ******** cue *** **** ******** ** *** naked ** **** *****, ******* *** considerable ********** ** ************** ** **** a ** *****. ****** **** **** advancements ** ** ******* ********, ** introduce * ********** *********** ******** (***** body) **** ***-******** ********** (****, ******** shape *** *******) ** ** ******* humans.
**** ******** ******** *** ***** ********* modalities, *** ********* **** * ****** fusion ******** ** ********* *** **** match **** ***** ***** **********. ********* the **** ******* ****** ***** ********** identifies *** **** ******** *****.
****** ****** ****** ************* ***********, ******** to ******* ********** *** ******** *** most ******** ********. **’* **** ******** and *******-*********.
*** **** **** *** ********* *** determine ***** ********* ******** ** *** based ** * ******'* ***** ******* the ****** (*****, ****, ** **** view) *** ************* ******* ********** ********** for **** ********.
*** ********, ** **** *** **** of * ******'* **** ** *******, and *** **** ** ********, ******** relies ** **** *****. ** **** scenarios, **** ***** ******* * ******** factor **** *********** *** ***** ********** of ***********. *********, **** * ****** is ****** **** *** ****, **** recognition ****** **** ******** **** **** viewed **** *** *****. **** ** because *** ****, ** ****** ** walking, ** **** *********** **** * side ****. ** * ******, *** gait ** ***** ****** ******** ***** the ********** ** **** *****. ** essence, ******** ******** **** ******** ************, determining *** ******* ***** ** ******, body *****, *** **** ***********. **** process ** *********, ******** *** ******, rather **** ***** *********, ** ****** the **** ********* ******** *********** *** each ****** *********.
*** ***** **** ***** ****** ********* of ****** ********* *** ********* *********** accuracy, ****** **** ******** **** ** evaluate **** *** **** *****.
* ****** ******** *** **** ** better ******** *** **** ***** *** the ****, ******* *** ********* ** seeing *** ****** **** ********* **** over * **** **** ** ******* the **** ***** *** *** ****.
Biometric ************** ********
******** ******* **** * **** ******** rate ** ~**%, **** ***** ********* set ** * *% ****. ******** to ***** ******, ******** ***** ** increase ** ~**% ** ********. ****** results **** *** ***** **** ********** and ****** ***** ** ********* ***********.
*** ********** ***** ******* ******* ********* with ****** *** *** *** ******* of * ****-******** ******. ** **********, the ******* ***** ******* ****-***** ************ images. ********'* *********** ****** ******* ** the ******** *********.
Computing ********* ******
******** ******** ** ***** ** ********* more ********* **** ********** ****** ***********, and ****** ********* ****** ********* *********. Liu **** **** **** ********, *** a ****** ***** ******, **** ** a ****** ** **** *** **** at ** ** ** ***, *** further ************* *** ********.
******** **** ** * ****** ** with *** *** *****, ** ***** 10 ** ** ****** *** ******. It's *** ******** **** ****, *** I ***** ** ** **** ** make ** **** ****, ** ***** do **.
FarSight ** ** *********** ** ****** ********* ******
******** *** ** **** *** ******** security ** ** *********** ** ****** attribute ******, ***** * ****** ** video ******* *** ** ******** ****** different ****-****** *** ******* ********** ** clothing *******. **** ******** ** ****** for ******** ************ ***********, *** ** include **** ** * *********. *** example, **** * ****** *** ** used ** ****** ** ***** *****.
** *** ***** ****, ******** ** not * ****** *********** ** ****** recognition. ***** ******** **** ****** ***********, in ***** ** *** * ****** to * *********, ***** ***** **** to ****** *** ******'* **** *** body-shape ******* ***** **** ***** **** image, ***** ***** *** ******* *********. In ***** ****** **** ***** ****** views, ****** *********** ***** ** ** effective ** ******** *** ***** ***********. FarSight *** ~*% ****** ******** **** using **** ****** *********** *** ****** where *** **** **** ** *******.
Training **** ******* **********
*** **** **** **** *** ******* limitation ** ********* *********** ** *** training ****, ** *** ******** **** that ** ********* **** *** **** long-range **************. ******** *********** **** *********** their *** ******** ** ******* ***** constraints ** ******* ********.
*** ******* ********** ** *** ****** gap, ** ******** ********* ***'* ***** the ******* ** *****'* ******. **'** addressed **** ** ************ *** **** for *** ****** ******. ************* ******* like ******** **** ****** ***********. *** focus ** *** ** ************* *********** systems, *********** ********** **** *** *** ethnicity **** ******* ************** ** ***********.
Open ** *********
******** ******** *** ** ******** *** commercialized **** ***** ******** ************ *** a *** ****** ** ******* ******* for ******** ********, ********* ** ***.
***************** ** ******** ******* ** ******* interest. **'** **** ** ********* *** technologies ** ********** *********... ******** ******** isn’t *** ***, ** ******** ************ are *****. * *** ****** ** focused ****** *** **** ***** ************ viable *** ******** ********.
Future ************
*** ****, **** ** *** ******* Phase * ** *** *******, **** have ******** *** ******** ** *** algorithm *** *** *** ***** ** efficiency, ** ********** ** "****** ** enhance."
**'** ******** ******** ************* ** ***** 2. *** ******** *** **** **** on ******** **** **********, ** *** latter ** ****** ** *******. **** more **** *** ******** ************* ****** like ************, **'** ****** ******* ************.