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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 iOS platform uses mobilenet by default.

Input data characteristics:

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

IOS#

  Performance measurements for the ARM architecture of iPhone X are presented in the tables below. The measured values represent averages of at least 60 experiments. Mobilenet is used by default. The auto setting for the number of threads indicates that the maximum number of available threads will be used. To enable this mode, set the numThreads parameter to -1 in runtime.conf. This configuration corresponds to the number of available processor cores. We strongly recommend that you follow this recommendation. Otherwise, performance may be significantly reduced. You can find descriptions of the corresponding settings in "Configuration Guide - Runtime settings".

The tables below show performance metrics for iPhone 11 (128 GB).

Matcher performance#

Type CPU threads Batch Size Percentile 95 (matches/sec) RAM Memory (Mb)
59 1 1 6503020 361
60 1 1 6588440 361

Extractor performance#

Type CPU threads Batch Size Percentile 95 (ms) RAM Memory (Mb)
59 1 1 45.66 144
59 auto 1 46.85 156
59 auto 4 48.15 180
59 auto 8 50.33 255
60 1 1 46.12 294
60 auto 1 46.15 317
60 auto 4 45.4 339
60 auto 8 45.54 361

Detector performance#

Type CPU threads Batch Size Percentile 95 (ms) RAM Memory (Mb)
Detector 1 1 22.54 91
Detector auto 1 22.58 96
Detector auto 4 23.27 118
Detector auto 8 23.26 144

Estimations performance with batch interface#

Type CPU threads Batch Size Percentile 95 (ms) RAM Memory (Mb)
AGS 1 1 0.5 39
AGS auto 1 0.49 43
AGS auto 4 0.48 48
AGS auto 8 0.47 55
BestShotQuality 1 1 0.94 55
BestShotQuality auto 1 0.94 60
BestShotQuality auto 4 0.93 68
BestShotQuality auto 8 0.92 76
Eyes 1 1 1.84 144
Eyes auto 1 1.83 144
Eyes auto 4 1.83 144
Eyes auto 8 1.79 144
Glasses 1 1 2.54 361
Glasses auto 1 2.45 361
Glasses auto 4 2.42 361
Glasses auto 8 2.38 361
HeadPose 1 1 0.54 361
HeadPose auto 1 0.54 361
HeadPose auto 4 0.54 361
HeadPose auto 8 0.52 361
MedicalMask 1 1 14.74 361
MedicalMask auto 1 14.73 361
MedicalMask auto 4 14.18 361
MedicalMask auto 8 14.0 361
Quality 1 1 2.8 361
Quality auto 1 3.03 361
Quality auto 4 2.97 361
Quality auto 8 2.9 361
Warper 1 1 1.05 361
Warper auto 1 1.07 361
Warper auto 4 1.02 361
Warper auto 8 1.2 361
LivenessOneShotRGBEstimator 1 1 167.81 361
LivenessOneShotRGBEstimator auto 1 168.15 361
LivenessOneShotRGBEstimator auto 4 169.31 363
LivenessOneShotRGBEstimator auto 8 170.31 559
Mouth 1 1 18.98 575
Mouth auto 1 18.98 575
Mouth auto 4 18.44 575
Mouth auto 8 18.39 580
FaceOcclusion 1 1 16.21 580
FaceOcclusion auto 1 16.29 580
FaceOcclusion auto 4 15.82 580
FaceOcclusion auto 8 15.86 580

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 59 (60) 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 only in the complete edition.

The table below shows FaceEngine features supported by the complete edition for mobile platforms.

"Feature matrix"

Facility Module Complete
Core Yes
Face detection & alignment Face detector Yes
Parameter estimation BestShotQuality estimation Yes
Color estimation Yes
Eye estimation Yes
Head pose estimation Yes
AGS estimation Yes
LivenessOneShotRGB estimation Yes
Medical Mask estimation Yes
Quality estimation Yes
Mouth estimation Yes
Glasses estimation Yes
Face descriptors Descriptor extraction Yes
Descriptor matching Yes
Descriptor batching Yes
Descriptor search acceleration Yes

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