Amazon Rekognition Facial Detection Tested

By: IPVM Team, Published on Dec 03, 2018

Amazon's Rekognition service has announced improvements, that if accurate, would be a significant advance over conventional facial detection.

amazon recognition

In particular, Amazon claims:

Amazon Rekognition can now detect 40 percent more faces – that would have been previously missed – in images that have some of the most challenging conditions described earlier... These aspects might include pose variations caused by head movement and/or camera movements, occlusion due to foreground or background objects (such as faces covered by hats, hair, or hands of another person in the foreground), illumination variations (such as low contrast and shadows), bright lighting that leads to washed out faces, low quality and resolution that leads to noisy and blurry faces, and distortion from cameras and lenses themselves. [emphasis added]

IPVM developed a dataset of 100+ 3-second video clips:

facial detection dataset 2

These clips are done across a number of common real-world scenarios:

  • Day light (200 lux)
  • Low light (1 lux)
  • Side tilt (from 0° to 90°)
  • Down tilt (from 0° to 30°)

We compiled various combinations (such as 200 lux, 15° side tilt, 22° down tilt or 1 lux, 75° side tilt, 30° down tilt, etc.).

We then ran tests of these 100+ clips across facial detection approaches:

  • HAAR 
  • HOG
  • Openvino (specifically the face-detection-retail-0004 model on Intel i7 and Myriad X chips expanded upon from our NCS2 test)
  • Amazon Rekognition

Inside, we explain full results and key variances in performance.

******'* *********** ******* *** ********* improvements, **** ** ********, would ** * *********** advance **** ************ ****** detection.

amazon recognition

** **********,****** ******:

****** *********** *** *** detect ** ******* **** faces – **** ***** have **** ********** ****** ** ****** **** have **** ** *** most *********** ********** ********* earlier... These ******* ***** *******pose ********** caused by head movement and/or camera movements, ************ ** ********** ** background ******* (**** ** faces ******* ** ****, hair, ** ***** ** another ****** ** *** foreground),illumination ********** (such as low contrast and shadows), bright lighting that leads to washed out faces, low ******* and resolution that leads to noisy *** ****** *****, and distortion from cameras and lenses themselves. [emphasis added]

**** ********* * ******* of ***+ *-****** ***** clips:

facial detection dataset 2

***** ***** *** **** across * ****** ** common ****-***** *********:

  • *** ***** (*** ***)
  • *** ***** (* ***)
  • **** **** (**** *° ** 90°)
  • **** **** (**** *° to **°)

** ******** ******* ************ (such ** *** ***, 15° **** ****, **° down **** ** * ***, 75° **** ****, **° down ****, ***.).

** **** *** ***** of ***** ***+ ***** across ****** ********* **********:

  • **** 
  • ***
  • ******** (************ *** ****-*********-******-**** ***** ** Intel ** *** ****** X ***** ******** ******** *** **** ****)
  • ****** ***********

******, ** ******* **** results *** *** ********* in ***********.

[***************]

Key ********

****** *********** ****** ********* ******** was *** ****** **** all ****** ****** ********* nearly ** **** ** Openvino:

facial detection overall accuracy

*** ****** *** ********* quite '***' *** **** of *** ******** ** used **** ***** ****, including *** ********* *** bad ******.

*** *******, **** ** segment *** ****** ** key ********** ****, **** for ****** ***********, ***** differences ** *********** ******* are *******:

rekognition accuracy segmented

**** ******* *** *** severe ****** ******** ***, with '****' ***** *********** at ****** **% ***** low ***** *********** ** under **%.

***** *********** *** ********* in ****** ********* ********, it ***, *** *** away, *** *******, ** two ****** ** ********* versus ******** *** **** ******* on ** **: 

facial detection fps

*********** ** **** ****** **** the ***** ******** ** the *********** ***** ******* ******** the ***** ** ** sent ***** ** *** while *** ***** ******** can ** **** ******* / ** *** **** / ***** *** ****** is. ** ******* ***** measurement ******* ** *** final ******* ** **** report.

********

*****, ** ******* ** 100% ******** *** ****** Rekognition, ***** ** ****** strong *********** *** **** is **° **** *** 45° **** **** **** the ******:

[video-to-gif output image]

****, ** ******* ** 59% ******** (******* ~* out ** ** ****** correctly ******* * ****) for ****** ***********, ****** a ***** ********* ** steep **** *** **** tilt **** *** *******'* head ****:

*** ***** ******, ** noted, **** **** **** challenging **** *** ***********. For *******, **** * out ** ***** * frames *** * **** accurately ******** ****** **** a ***** ***** *** the ****** / ********:

 

*******, *** ** *** few **** ************ *** Rekognition *** ** **** test ********, ****** *** light, *** ******* ****** straight ******* *** ****** but ***** **** *** not ******** ** ***:

Accuracy ***********

*** ******** ****** **** is ************** *** * simplification. *********** **** ********* evaluation **** * ****** called"**** ******* *********" (***), ***** ******** ***********-**-******* on ****** **** ********* datasets ********* ***** ** ****** **** bounding-box ******* (********* ****** "Intersection **** *****" ** IoU) *** ******* **** box ********* ******** * face. ** ****'* ***** library, **** **** ****** has *** **** (***** frame *** * ****, and ***** ** **** one). ***** ****, ** can *** * ****** heuristic *** ********: ** how **** ****** *** a **** ******** (******** / *****-***** = ********). This ** *** *******: in **********, ** **** not ******* *** ***** positives (* **** ** detected ***********, **** ** the **** ** ** the *******'* *****). **** happens ****** - ** we ***** **** *** heuristic. ** ****** ** more ******* **** ********** (lux, *****, ***) ******* having ** ***** ******** boxes *** ***** ***** of *** *******.

FPS ***********

*** ******* **** ****** used *** *********** ***:

  1. ****** *** ***** ** Amazon **
  2. ***** *** *****
  3. ***** *********** (******** ** ** the ** ****, *********** an***->***********)
  4. **** *** *****, ********* FPS

* **** ****** ******** for ************ ***** ** to ****** ****** ******** via *******->***********. **** ***** *** the ********** ******** ** a *****-**-***** *****, ***** our ***** ***** *** networking ******** **** *** FPS *********** ********. ** such, *** *** ***********, already **** ***, ***** be **** *****.

***** ** **** ************, this ** * ***** point ******* ***********, ** others ** *** ** a *******. *** *******, one *** *** ***-***** hardware *** ****** ******* performance. *** ***** ****** in **** ******* *** on ****** * ** Intel ** ******* (****-***) devices, ** **** *********** trade-off ** *********-*********. ************, Rekognition ** * ******* service, ***** *********** * model (**** ********'* ****-******-****) takes **** ************* *** setup. ** ***** ****-**** performance **** *********** ** ******** (note: ** *** ***** investigating ** **** ** possible), *** ***-****-**** ************, Rekognition ***** ** ***** useful.

*******

*** * ****-**** ***** surveillance ***********,*********** ************ ** ***** **** but *** ***** ************ where **** ********** ***** processing ** ******, ** could ** *** **** affordable **** **-**** **********. For *******, *********** ******* for **-**** (*. ********) is $0.12 *** * *** of **** ****** ***** analyzed. **** **** **** 10% ******** ** ******* (i.e., *,*** ******* ** a *****), *** ******* **** would ** ~$** (*,*** x **% * $*.**) just *** *** **** analytics.

** *** ***** ****,****** *********** ****** ******* ***************** ****** ***********, ****** analysis (***, ******, *******), object *** ********** *********, etc. **** **** ********* provider ****** ** *** have ** ***** ** likely ** ******** ** compete **** ***** ***********.

********

**** ** *** *** of ****'* *** ****** of **** ******** *****, starting **** ****** ********* *********** ****** ******* ***** 2 / ******** ** Test *** *** ****. ** are ******** * ******* of *** ***** ** the ******** ******. *** feedback, ******** ** *********, please *** ** *** comments.

Comments (7)

*** ***** ** *** interested ** *** ************,***** *** *** *** class **** ****:

deep learning webinar2

***** **** ** ********** fundamentals ** **** ** our ******** ****** ** provide ************* ******** *** education, ** ******** ** our ******* *****.

*** *** ******* **** running *** ********** **** IR *********** **? **** cameras ***** ****** ** IR *********** (** **** have *** ******) ** 1 ***

** *** *** ** any ** ******** *** we **** *** **** in ****** *****.

**** **** ***** **** integrate ** **** **** if **** *****'* *******.

** ***** *** *** you *** **** *** of **** *** ***** used ** *** ***** for **** ***** ** download *** *** **** their *** ********* *******?

* ***** **** ***** be * ****** ***** way ** ******* ********.

** ***** ******* *** dataset *** **** *** decided ***.

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