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Appendix A. Specifications#

Runtime performance for mobile environment#

Face detection performance depends on input image parameters such as resolution and bit depth as well as the size of the detected face. The Android platform uses mobilenet by default.

Input data characteristics:

  • Image resolution: 640x480px;
  • Image format: 24 BPP RGB;

Android#

Performance measurements are presented for ARM of Samsung SM-G930F and Samsung SM-J730FM in tables below. Measured values are averages of at least 100 experiments. Mobilenet is used by default. The number of threads auto means that will be taken the maximum number of available threads. For this mode use the -1 value for the numThreads parameter in the runtime.conf. This number of threads is equal to according number of available processor cores. We strongly recommend you to follow this recommendation; otherwise, performance can be significantly reduced. Description of according settings you can find in "Configuration Guide - Runtime settings".

Samsung SM-G930F. Matcher performance#

The table below shows the performance of Matcher on the Samsung SM-G930.

Measurement Model Threads Average Units
Matcher 59 - 60 K matches/sec
Matcher 60 - 60 K matches/sec

Samsung SM-G930F. Extractor performance#

The table below shows the performance of Extractor on the Samsung SM-G930.

Measurement Model Threads Batch Size Average (ms)
Extractor 59 1 1 606.4
Extractor 59 auto 1 241.2
Extractor 59 auto 4 248.0
Extractor 59 auto 8 250.9
Extractor 60 1 1 552.4
Extractor 60 auto 1 208.2
Extractor 60 auto 4 221.0
Extractor 60 auto 8 220.0

Samsung SM-G930F. Detector performance#

The table below shows the performance of Detector on the Samsung SM-G930.

Measurement Threads Average (ms)
Detector (FaceDetV2) 1 78.9 / 70.8 / 286.0
(Easy/complex/6 faces) auto 80.2 / 70.8 / 149.5

Samsung SM-G930F. Estimations performance#

The table below shows the performance of Estimations on the Samsung SM-G930 for estimators that have a batch interface.

Measurement Threads BatchSize Average (ms)
HeadPose 1 1 6.2
HeadPose auto 1 3.8
HeadPose auto 4 3.0
HeadPose auto 8 2.9
Eyes (RGB, useStatusPlan=0) 1 1 37.8
Eyes (RGB, useStatusPlan=0) auto 1 15.5
Eyes (RGB, useStatusPlan=0) auto 4 12.8
Eyes (RGB, useStatusPlan=0) auto 8 12.5
Eyes (RGB, useStatusPlan=1) 1 1 37.8
Eyes (RGB, useStatusPlan=1) auto 1 13.1
Eyes (RGB, useStatusPlan=1) auto 4 12.7
Eyes (RGB, useStatusPlan=1) auto 8 14.0
AGS 1 1 1.47
AGS auto 1 1.51
AGS auto 8 0.81
BestShotQuality 1 1 5.8
BestShotQuality auto 1 2.3
BestShotQuality auto 4 1.5
BestShotQuality auto 8 1.4
MedicalMask 1 1 236.4
MedicalMask auto 1 63.4
MedicalMask auto 4 60.1
MedicalMask auto 8 59.1
OneShot Liveness 1 1 2704.0
OneShot Liveness auto 1 939.4
OneShot Liveness auto 8 426.1
Glasses 1 1 3.07
Glasses auto 1 3.46
Glasses auto 4 3.09
Glasses auto 8 3.04

The table below shows the performance of Estimations on the Samsung SM-G930 for estimators that do not have a batch interface.

Measurement Threads Average (ms)
Warper 1 23.3
Warper auto 23.7
Quality 1 42.0
Quality auto 11.0

Samsung SM-J730FM. Matcher performance#

The table below shows the performance of Matcher on the Samsung SM-G930.

Measurement Model Threads Average Units
Matcher 59 - 60 K matches/sec
Matcher 60 - 60 K matches/sec

Samsung SM-J730FM. Extractor performance#

The table below shows the performance of Extractor on the Samsung SM-G930.

Measurement Model Threads Batch Size Average (ms)
Extractor 59 1 1 896.9
Extractor 59 auto 1 460.8
Extractor 59 auto 4 465.3
Extractor 59 auto 8 236.7
Extractor 60 1 1 760.0
Extractor 60 auto 1 420.8
Extractor 60 auto 4 420.3
Extractor 60 auto 8 210.1

Samsung SM-J730FM. Detector performance#

The table below shows the performance of Detector on the Samsung J730FM.

Measurement Threads Average(ms)
Detector (FaceDetV2) 1 89.6 / 82.2 / 321.3
(Easy/complex/6 faces) auto 100.2 / 82.5 / 144.2

Samsung SM-J730FM. Estimations performance with batch interface#

The table below shows the performance of Estimations on the Samsung SM-J730FM for estimators that have a batch interface.

Measurement Threads BatchSize Average(ms)
HeadPose 1 1 5.1
HeadPose auto 1 3.5
HeadPose auto 4 2.3
HeadPose auto 8 2.1
Eyes (RGB, useStatusPlan=0) 1 1 24.8
Eyes (RGB, useStatusPlan=0) auto 1 13.4
Eyes (RGB, useStatusPlan=0) auto 4 11.5
Eyes (RGB, useStatusPlan=0) auto 8 11.4
Eyes (RGB, useStatusPlan=1) 1 1 24.7
Eyes (RGB, useStatusPlan=1) auto 1 13.8
Eyes (RGB, useStatusPlan=1) auto 4 11.5
Eyes (RGB, useStatusPlan=1) auto 8 11.4
AGS 1 1 1.47
AGS auto 1 1.51
AGS auto 8 0.81
BestShotQuality 1 1 5.0
BestShotQuality auto 1 3.2
BestShotQuality auto 4 2.5
BestShotQuality auto 8 2.0
MedicalMask 1 1 157.0
MedicalMask auto 1 62.0
MedicalMask auto 4 56.0
MedicalMask auto 8 58.1
OneShot Liveness 1 1 5007.7
OneShot Liveness auto 1 887.7
OneShot Liveness auto 8 894.9
Glasses 1 1 8.97
Glasses auto 1 5.99
Glasses auto 4 5.8
Glasses auto 8 5.77

Samsung SM-J730FM. Estimations performance without batch interface#

The table below shows the performance of Estimations on the Samsung SM-J730FM for estimators that do not have a batch interface.

Measurement Threads Average (ms)
Warper 1 14.5
Warper auto 14.5
Quality 1 31.0
Quality auto 13.0

Descriptor size#

The table below shows size of serialized descriptors to estimate memory requirements.

"Descriptor size"

Descriptor version Data size (bytes) Metadata size (bytes) Total size
CNN 62 512 8 520

Metadata includes signature and version information that may be omitted during serialization if the NoSignature flag is specified.

When estimating individual descriptor size in memory or serialization storage requirements with default options, consider using values from the "Total size" column.

When estimating memory requirements for descriptor batches, use values from the "Data size" column instead, since a descriptor batch does not duplicate metadata per descriptor and thus is more memory-efficient.

These numbers are for approximate computation only, since they do not include overhead like memory alignment for accelerated SIMD processing and the like.

Feature matrix#

Mobile versions come in two editions: the frontend edition (or FE for short) and the complete edition.

The table below shows FaceEngine features supported by different editions of mobile platform.

"Feature matrix"

Facility Module Complete Frontend
Core Yes Yes
Face detection & alignment Face detector Yes Yes
Parameter estimation BestShotQuality estimation Yes Yes
Color estimation Yes Yes
Eye estimation Yes Yes
Head pose estimation Yes Yes
AGS estimation Yes Yes
LivenessOneShotRGB estimation Yes Yes
Medical Mask estimation Yes Yes
Quality estimation Yes Yes
Glasses estimation Yes Yes
Face descriptors Descriptor extraction Yes No
Descriptor matching Yes No
Descriptor batching Yes No
Descriptor search acceleration Yes No

See file "doc/FeatureMapMobile.htm" for more details.