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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 for the ARM of the Samsung Galaxy A52s SM-A528B are presented in the tables below. The measured values are averages of at least 100 experiments. Mobilenet is used by default. The number of threads set to auto means that the maximum number of available threads will be used. For this mode, set the numThreads parameter in runtime.conf to -1. This number of threads equals the number of available processor cores. We strongly recommend that you follow this advice. Otherwise, performance may be significantly reduced. For setting descriptions, see "Configuration Guide - Runtime settings".

Samsung Galaxy A52s SM-A528B, arm64. Matcher performance#

The table below shows the performance of Matcher on Samsung Galaxy A52s SM-A528B.

Type Model CPU threads Batch Size Average (matches/sec)
Matcher 59 1 1 1.25M
Matcher 60 1 1 1.67M

Samsung Galaxy A52s SM-A528B, arm64. Extractor performance#

The table below shows the performance of Extractor on Galaxy A52s SM-A528B.

Type CPU threads Batch Size Average (ms) RAM Memory (Mb)
59 1 1 60.17 57.0
59 auto 1 31.06 58.0
59 auto 4 25.49 67.0
59 auto 8 23.38 96.0
60 1 1 54.39 53.0
60 auto 1 27.76 54.0
60 auto 4 22.61 55.0
60 auto 8 20.88 75.0

Samsung Galaxy A52s SM-A528B, arm64. Detector performance#

The table below shows the performance of Detector on Samsung Galaxy A52s SM-A528B.

Measurement Threads Average (ms)
Detector (FaceDetV2) 1 10.0 / 36.8 / 38.9
(Easy/complex/6 faces) auto 8.95 / 27.5 / 35.4

Samsung Galaxy A52s SM-A528B, arm64. Estimations performance#

The table below shows the performance of Estimations on Samsung Galaxy A52s SM-A528B for estimators that have a batch interface.

Type CPU threads Batch Size Average (ms) RAM Memory (Mb)
HeadPose 1 1 0.54 44.0
HeadPose auto 1 1.15 43.0
HeadPose auto 4 0.63 61.0
HeadPose auto 8 0.4 85.0
Warper 1 1 2.54 38.0
Warper auto 1 3.02 38.0
Warper auto 4 2.9 41.0
Warper auto 8 1.24 43.0
Eyes 1 1 2.82 40.0
Eyes auto 1 1.74 40.0
Eyes auto 4 1.22 39.0
Eyes auto 8 1.16 40.0
Eyes 1 1 2.84 40.0
Eyes auto 1 1.59 40.0
Eyes auto 4 1.24 39.0
Eyes auto 8 1.24 40.0
AGS 1 1 0.56 44.0
AGS auto 1 1.13 43.0
AGS auto 4 0.36 61.0
AGS auto 8 0.31 85.0
BestShotQuality 1 1 1.46 46.0
BestShotQuality auto 1 1.45 45.0
BestShotQuality auto 4 0.82 63.0
BestShotQuality auto 8 0.5 87.0
MedicalMask 1 1 16.83 64.0
MedicalMask auto 1 11.47 64.0
MedicalMask auto 4 8.25 81.0
MedicalMask auto 8 7.01 105.0
Quality 1 1 2.88 39.0
Quality auto 1 3.29 39.0
Quality auto 4 3.46 39.0
Quality auto 8 3.13 39.0
Glasses 1 1 3.16 40.0
Glasses auto 1 4.15 40.0
Glasses auto 4 3.29 40.0
Glasses auto 8 3.76 40.0
LivenessOneShotRGBEstimator 1 1 211.37 82.0
LivenessOneShotRGBEstimator auto 1 89.38 96.0
LivenessOneShotRGBEstimator auto 4 84.54 212.0
LivenessOneShotRGBEstimator auto 8 85.35 371.0
DepthLivenessEstimator 1 1 6.37 21.0
DepthLivenessEstimator auto 1 9.38 21.0
DepthLivenessEstimator auto 4 7.46 27.0
DepthLivenessEstimator auto 8 5.53 35.0
Mouth 1 1 21.42 48.0
Mouth auto 1 13.06 48.0
Mouth auto 4 9.89 54.0
Mouth auto 8 9.26 68.0
FaceOcclusion 1 1 21.24 40.0
FaceOcclusion auto 1 13.66 40.0
FaceOcclusion auto 4 11.97 71.0
FaceOcclusion auto 8 12.07 108.0

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
Mouth 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.