Performance Parameters

List of abbreviations:

• FAR (false accept rate) – the probability that the system incorrectly accepts a non-authorized person.
• TAR (true accept rate) – the probability that the system correctly accepts an authorized person.
• FRR (false reject rate) – the probability that the system incorrectly rejects an authorized person.
• IR (identification rate) – identification rate.

Capturer Characteristics

Capturer Configuration Files

Click here to see the list of the capturer configuration files

File	Detector	Set of points	Angles (roll/yaw/pitch)	Description and use case
common_capturer4_fda.xml	lbf	fda	[-30;30][-60;60][-60;60]	Frontal face detector.
common_capturer4_fda_with_angles.xml	lbf	fda	[-90;90][-60;60][-60;60]	Frontal face detector. Adapted for a wide range of head rotation angles.
common_capturer4_fda_with_angles_noise.xml	lbf	fda	[-90;90][-60;60][-60;60]	Frontal face detector. Adapted for a wide range of head rotation angles. Suitable for images with high noise level.
common_capturer4_fda_singleface.xml	lbf	fda	[-30;30][-60;60][-60;60]	Only one frontal face is detected.
common_capturer4_fda_singleface_with_angles.xml	lbf	fda	[-90;90][-75;75][-60;60]	Only one frontal face is detected. The detector is adapted for a wide range of head rotation angles.
common_capturer4_fda_singleface_with_angles_noise.xml	lbf	fda	[-90;90][-75;75][-60;60]	Only one frontal face is detected. The detector is adapted for a wide range of head rotation angles. Suitable for images with high noise level.
common_capturer4_lbf.xml	lbf	doublelbf	[-30;30][-60;60][-60;60]	Frontal face detector.
common_capturer4_lbf_singleface.xml	lbf	doublelbf	[-30;30][-60;60][-60;60]	Only one frontal face is detected.
common_capturer4_mesh.xml	lbf	mesh	[-30;30][-60;60][-60;60]	Frontal face detector that allows you to get a 3D face mask.
common_capturer4_mesh_with_angles.xml	lbf	mesh	[-90;90][-60;60][-60;60]	Frontal face detector adapted for a wide range of head rotation angles that allows you to get a 3D face mask.
common_capturer4_mesh_with_angles_noise.xml	lbf	mesh	[-90;90][-60;60][-60;60]	Frontal face detector adapted for a wide range of head rotation angles and suitable for images with high noise level. The detector allows you to get a 3D face mask.
common_capturer4_mesh_singleface.xml	lbf	mesh	[-30;30][-60;60][-60;60]	Only one frontal face is detected. The detector allows you to get a 3D face mask.
common_capturer4_mesh_singleface_with_angles.xml	lbf	mesh	[-90;90][-75;75][-60;60]	Only one frontal face is detected. The detector is adapted for a wide range of head rotation angles and allows you to get a 3D face mask.
common_capturer4_mesh_singleface_with_angles_noise.xml	lbf	mesh	[-90;90][-75;75][-60;60]	Only one frontal face is detected. The detector is adapted for a wide range of head rotation angles, suitable for images with high noise level and allows you to get a 3D face mask.
common_capturer_blf_fda_front.xml	blf	fda	[-70;70][-90;90][-70;70]	Detection of large face images (the face should take up most of the frame size). Suitable for detection of masked faces.
common_capturer_blf_fda_back.xml	blf	fda	[-70;70][-90;90][-70;70]	Detection of several faces or small face images. Suitable for detection of masked faces.
common_capturer_blf_fda_auto.xml	blf	fda	[-70;70][-90;90][-70;70]	Detection of large and small face images (the parameters `resolution_width` and `min_face_size` should be specified in the configuration file). Suitable for detection of masked faces.
common_capturer_refa_fda_a.xml	refa	fda	[-70;70][-90;90][-70;70]	Face detector recommended for use in expert systems. The detector provides face detection with the largest coverage of rotation angles and maximum quality (including masked faces).
common_capturer_uld_fda.xml.xml	uld	fda	[-70;70][-90;90][-70;70]	Detection of large and small face images. Suitable for detection of masked faces.
common_video_capturer_fda.xml	lbf	fda	[-30;30][-60;60][-60;60]	Frontal face video tracker (RGB only).
common_video_capturer_lbf.xml	lbf	singlelbf	[-30;30][-60;60][-60;60]	Frontal face video tracker (RGB only).
common_video_capturer_mesh.xml	lbf	mesh	[-30;30][-60;60][-60;60]	Frontal face video tracker (RGB only) that allows you to get a 3D face mask.
fda_tracker_capturer.xml	lbf	fda	[-30;30][-60;60][-60;60]	Frontal face video tracker.
fda_tracker_capturer.w.xml	lbf	fda	[-30;30][-60;60][-60;60]	Frontal face video tracker that can be used in case of insufficient lighting. Note that false detections can occur a bit more often in this case.
fda_tracker_capturer_mesh.xml	lbf	fda	[-30;30][-60;60][-60;60]	Frontal face video tracker that allows you to get a 3D face mask.
fda_tracker_capturer_fake_detector.xml	lbf	fda	[-30;30][-60;60][-60;60]	Detection speed is higher because only fitter is used (no detector). Suitable only if a face takes up most of the image size.
fda_tracker_capturer_blf.xml	blf	fda	[-30;30][-60;60][-60;60]	Frontal face video tracker. Suitable for detection of masked faces.
fda_tracker_capturer_refa_a.xml	refa	fda	[-70;70][-90;90][-70;70]	Frontal face video tracker. Recommended for use in expert systems. The tracker provides face detection with the largest coverage of rotation angles and maximum quality (including masked faces).
fda_tracker_capturer_uld_fda.xml.xml	uld	fda	[-70;70][-90;90][-70;70]	Frontal face video tracker that can be used to detect faces of different size. Suitable for detection of masked faces.
manual_capturer_fda.xml	lbf	fda	[-30;30][-60;60][-60;60]	Eye points should be manually specified. The remaining points are calculated based on the eye points.
manual_capturer_mesh.xml	lbf	mesh	[-30;30][-60;60][-60;60]	Eye points should be manually specified. The remaining points are calculated based on the eye points that allows you to get a 3D face mask.
video_worker_fdatracker_refa_fda.xml	refa	fda	[-70;70][-90;90][-70;70]	Face detector recommended for use in expert systems. The detector provides face detection with the largest coverage of rotation angles and maximum quality (including masked faces).

Capturer Timing Characteristics

Core i7 4.5 GHz (Single-Core)

Configuration file	Capture time (ms)
	640x480, 1 face	640x480, 4 faces	1280x720, 1 face	1280x720, 4 faces	1920x1080, 1 face	1920x1080, 4 faces
common_capturer4_fda.xml	13	25	34	49	81	103
common_capturer4_fda_with_angles.xml	282	387	260	356	273	370
common_capturer4_mesh.xml	18	47	39	72	87	735
common_capturer4_mesh_with_angles.xml	291	415	268	383	281	398
common_capturer_blf_fda_auto.xml	6-30	12-36	8-32	14-38	19-44	26-51
common_capturer_blf_fda_back.xml	30	36	32	38	44	51
common_capturer_blf_fda_front.xml	6	12	8	14	19	26
common_capturer_refa_fda_a.xml	644	650	512	518	580	586
common_capturer_uld_fda.xml (min_size=150)	12	18	13	19	21	28
common_capturer_uld_fda.xml (min_size=90)	58	70	60	73	77	91
common_capturer_uld_fda.xml (min_size=50)	253	272	253	273	281	302
common_capturer4_fda_singleface.xml	16	-	51	-	123	-
common_capturer4_mesh_singleface.xml	23	-	58	-	129	-

GPU

Configuration file	Capture time (ms)
	640x480, 1 face	640x480, 4 faces	1280x720, 1 face	1280x720, 4 faces	1920x1080, 1 face	1920x1080, 4 faces
common_capturer_blf_fda_auto.xml	4-5	10-12	6-8	13-14	17-20	24-27
common_capturer_blf_fda_back.xml	5	12	8	14	20	27
common_capturer_blf_fda_front.xml	4	10	6	13	17	24
common_capturer_refa_fda_a.xml	236	240	229	235	170	176
common_capturer_uld_fda.xml (min_size=150)	4	10	5	11	13	20
common_capturer_uld_fda.xml (min_size=90)	14	21	17	23	26	34
common_capturer_uld_fda.xml (min_size=50)	27	34	27	35	47	49

Note: Actual capture time may vary depending on the image content.

Identification Performance

Timing Characteristics for Core i7 4.5 GHz*

Recognition method	Template generation (ms)	Accelerated Matching 1:N (ms)			Matching 1:1 (ms)
		N = 104	N = 106	N = 107
6.7	40 (45**)	0.25	12.1	126	0.04
7.7	170 (180**)	0.25	12.1	126	0.04
8.7	20 (20**)	0.25	12.1	126	0.04
9.30	30	0.18	12.0	117	0.04
9.300	260 (125***)	0.18	12.0	117	0.04
9.1000	730 (305***)	0.18	12.0	117	0.04
9.30mask	20	0.18	12.0	117	0.04
9.300mask	160 (79***)	0.18	12.0	117	0.04
9.1000mask	290 (144***)	0.18	12.0	117	0.04
10.30	24 (16***)	0.18	12.0	117	0.04
10.100	40 (24***)	0.18	12.0	117	0.04
10.1000	690 (355***)	0.18	12.0	117	0.04
11.1000	865 (425***)	0.22	15.0	151	0.04

* – characteristics specified in this table are given for a single-core CPU.
** – template creation time when processing_less_memory_consumption was set to true in the FacerecService.createRecognizer call for recognizer creation.
*** – template creation time using the AVX2 instruction set (see Face identification).

Note:

• Accelerated search time is given for k=1. As for larger values of k, the time will increase up to the search time without acceleration.
• Accelerated search is implemented only for the recognition methods 6.5, 6.6, 6.7, 7.3, 7.6, 7.7, 8.6, 8.7, 9.30, 9.300, 9.1000, 10v30, 10v100, 10v1000, 11v1000.
• To achieve this speed, the templates in the index must be located in order of creation (by using the Recognizer.processing or Recognizer.loadTemplate method).
• To achieve higher speed, use GPU (see GPU Usage).

Memory Characteristics

Recognition method	Serialized template size (Bytes)	Template size in RAM (Bytes)	Memory consumption* (MB)
6.7	536	636	105 (85**)
7.7	536	636	195 (163**)
8.7	536	636	52 (40**)
9.30	280	380	155
9.300	280	380	210
9.1000	280	380	290
10.30	280	380	160
10.100	280	380	180
10.1000	280	380	270
11.1000	296	396	480

* – the amount of memory consumed doesn't depend on the number of the Recognizer objects created by this method
** – memory consumption when processing_less_memory_consumption was set to true in the FacerecService.createRecognizer call for recognizer creation

ROC on internal dataset with the faces in the wild

In this test the set of mismatched pairs was increased and LFW errors were fixed to get accurate measurements at low FAR.

FAR	9.300 TAR (%)	10.30 TAR (%)	10.100 TAR (%)	11.1000 TAR (%)
1e-4	98.3	95.3	97.3	99.7
1e-5	97.2	91.4	95.0	99.6
1e-6	94.8	86.3	91.7	99.3

Performance Test

Modes:

• Create a Configuration File
• Detection
• Processing
• 1:1 Recognition Test
• 1:N Recognition Test
• Search Speed Test

Create a Dataset Configuration File

• Dataset Configuration File is a text file with 3 lines per each image (<person_id> <image_id> <path_to_image>).

Example:

person0_id
image0_id
path_to_image0
person0_id
image1_id
path_to_image1
...

Example of creating an image dataset configuration file

Collect a list of image files

find -type f | sort > ../lfw_simple_format.txt

• lfw_simple_format.txt is a text file with the lines in the following format: <path/to/person/dir/image_file>. Images with the same path/to/person/dir belong to the same person. Images for one person must be in a row.

Example:

person1_dir/image1
person1_dir/image2
person2_dir/image1
...

The paths in the configuration files must be relative to the directory passed through the dataset_root_dir parameter.

Convert a list to a Configuration File

In this mode the program converts the config file from list format to dataset format (used in other modes):

Launch parameters:

• mode – program mode (convert_config_format)
• result_dataset_config – config file of the image dataset
• FILE – one file that contains a list of image files

Example of launch:

./test_sdk \
    --mode convert_config_format \
    --result_dataset_config dataset_config.txt \
    lfw_simple_format.txt

The conversion result is a config file of the image dataset.

Detection

In this mode the program detects faces in images with the id in the range of [begin_image_id, end_image_id). Each image should contain only one person. The images, containing more than one person detected, will be discarded.
Note: when using a GPU, it is recommended to run no more than one of any test at the same time.

ATTENTION

Before starting the program, you need to get the database file with images (see Create a Configuration File)

Launch parameters:

• mode – program mode (detection)
• dll_path – path to the libfacerec.so or facerec.dll library file
• sdk_config_dir – path to the conf/facerec directory
• dataset_config – config file of the image dataset (see Create a Configuration File)
• capturer_config – capturer config file name
• dataset_root_dir – path to the dataset directory
• detection_result_file – file for storing the detection results
• [begin_image_id] – index of the image, from which the detection begins (the default setting is 0)
• [end_image_id] – index of the image, to which detection is performed (by default, the processing is performed to the end of the file)
• [use_cpu_cores_count] – number of cores used for detection (the default setting is 1)

Example of launch from the bin directory:

./test_sdk \
    --mode detection \
    --dll_path ../lib/libfacerec.so \
    --sdk_config_dir ../conf/facerec \
    --capturer_config common_capturer4_lbf.xml \
    --dataset_config dataset_config.txt \
    --dataset_root_dir /path/to/data \
    --detection_result_file \
    detection_result.bin

The detection result is a text file with the lines in the following format: <image_id> <points_count> <points>

Processing

In this mode the program creates templates from the faces detected in the images with the id in the range of [begin_image_id, end_image_id).

ATTENTION

Before starting the program, you need to get the detections file for the recognizer under test (see Detection)

Launch parameters:

• mode – program mode (processing)
• dll_path – path to the libfacerec.so or facerec.dll library file
• sdk_config_dir – path to the conf/facerec directory
• dataset_config – config file of the image dataset (see Create a Configuration File)
• dataset_root_dir – path to the dataset directory
• recognizer_config – recognizer config file name
• processing_result_file – file for storing the resulting templates
• [begin_image_id] – index of the image, from which processing starts (the default setting is 0)
• [end_image_id] – index of the image, to which processing is performed (by default, the processing is performed at the end of the file)
• [use_cpu_cores_count] – number of cores used for processing (the default setting is 1)
• FILES – file(s) with the detection results

Note: a trial license allows to run the test only when [use_cpu_cores_count] is set to 1.

Example of launch from the bin directory:

./test_sdk \
    --mode processing \
    --dll_path ../lib/libfacerec.so \
    --sdk_config_dir ../conf/facerec \
    --dataset_config dataset_config.txt \
    --dataset_root_dir /path/to/data \
    --recognizer_config method6v7_recognizer.xml \
    --processing_result_file ./templates_6v7.bin \
    detection_result.bin

The processing result is a binary file, which contains one record per each generated template. Each record is a 64-bit unsigned integer (image_id) followed by a serialized template.

1:1 Recognition Test

In this mode the program performs the 1:1 recognition test using the templates generated from the images with the id in the range of [begin_image_id, end_image_id).

ATTENTION

Before running the test, it is necessary to calculate the template file for the recognizer under test (see Processing)

Launch parameters:

• mode – program mode (recognition_test_11)
• dll_path – path to the libfacerec.so or facerec.dll library file
• sdk_config_dir – path to the conf/facerec directory
• dataset_config – config file of the image dataset (see Create a Configuration File)
• recognizer_config – recognizer config file name
• result_roc_file – file to save the ROC curve
• result_closest_mismatches_file – result file with the closest mismatches
• [begin_image_id] – index of the first image used in the test (the default setting is 0)
• [end_image_id] – index of the first image after begin_image_id not used in the test (by default use all from begin_image_id to the end of the file)
• [use_cpu_cores_count] – number of cores used for matching (the default setting is 1)
• FILES – file(s) with the processing results

Note: a trial license allows to run the test only when [use_cpu_cores_count] is set to 1.

Example of launch from the bin directory:

./test_sdk \
    --mode recognition_test_11 \
    --dll_path ../lib/libfacerec.so \
    --sdk_config_dir ../conf/facerec \
    --dataset_config dataset_config.txt \
    --recognizer_config method6v7_recognizer.xml \
    --result_roc_file ./roc11_6v7.txt \
    --result_closest_mismatches_file ./closest_mismatches_file.txt \
    templates_6v7.bin

1:1 Recognition of test results:

• A text file, which contains a ROC curve; the file line describes the curve point in the following format: <far> <tar> <distance>.
• A text file with the lines in the following format: <distance> <image_id1> <image_id2> <path_to_image1> <path_to_image2>. This file contains pairs of images marked as belonging to different people, but having a minimum distance between the templates. We recommend you to generate this file using the best method and view the images from the top of it to check the errors of the dataset annotation.

Building a ROC curve

To draw a ROC curve, you need to run the draw_roc_curves utility (see Building a ROC curve)

1:N Recognition Test

In this mode the program performs the 1:N recognition test using the templates generated from the images with the id in the range of [begin_image_id, end_image_id).

ATTENTION

Before running the test, it is necessary to calculate the template file for the recognizer under test (see Processing)

Launch parameters:

• mode – program mode (recognition_test_1N)
• dll_path – path to the libfacerec.so or facerec.dll library file
• sdk_config_dir – path to the conf/facerec directory
• dataset_config – config file of the image dataset (see Create a Configuration File)
• recognizer_config – recognizer config file name
• result_roc_file – file to save the ROC curve.
• [begin_image_id] – index of the first image used in the test (the default setting is 0)
• [end_image_id] – index of the first image after begin_image_id not used in the test (by default use all from begin_image_id to the end of the file)
• [use_cpu_cores_count] – number of cores used for searching (the default setting is 1)
• [acceleration] – search acceleration type (the default setting is 0)
• 0 – search with pbio::Recognizer::search with the pbio::Recognizer::SearchAccelerationType::NO_SEARCH_ACCELERATION acceleration
• 1 – search with pbio::Recognizer::search with the pbio::Recognizer::SearchAccelerationType::SEARCH_ACCELERATION_1 acceleration -1 – search with pbio::Recognizer::verifyMatch in a single thread (independent of search_threads_count)
• FILES – file(s) with the processing results

Note: a trial license allows to run the test only when [use_cpu_cores_count] is set to 1. In addition, it may be needed to specify value for the [end_image_id] parameter so that "gallery_templates size" does not exceed 1000 (printed to the console after running the test).

Example of launch from the bin directory:

./test_sdk \
    --mode recognition_test_1N \
    --dll_path ../lib/libfacerec.so \
    --sdk_config_dir ../conf/facerec \
    --dataset_config dataset_config.txt \
    --recognizer_config method6v7_recognizer.xml \
    --result_roc_file ./roc1N_6v7.txt \
    --acceleration 1 \
    templates_6v7.bin

The result of the 1:N recognition test is a text file, which contains a ROC curve. The file line describes the curve point in the following format: <far> <tar> <distance>.

Building a ROC curve

To draw a ROC curve, you need to run the draw_roc_curves utility (see Building a ROC curve)

Search Speed Test

In this mode the program performs the search speed test using the templates generated in processing mode or using the utility (see Template Generator).

Launch parameters:

• mode – program mode (search_speed_test).
• dll_path – path to the libfacerec.so or facerec.dll library file
• sdk_config_dir – path to the conf/facerec directory
• recognizer_config – recognizer config file name
• [templates_count] – number of templates used (by default, processing is performed until the end of the file)
• [queries_count] – number of queries (the default setting is 1)
• [query_k_nearest] – number of the nearest templates for searching (the default setting is 1)
• [search_threads_count] – number of threads used for searching (the default setting is 1)
• [acceleration] – search acceleration type (the default setting is 0)
• 0 – search with pbio::Recognizer::search with the pbio::Recognizer::SearchAccelerationType::NO_SEARCH_ACCELERATION acceleration
• 1 – search with pbio::Recognizer::search with the pbio::Recognizer::SearchAccelerationType::SEARCH_ACCELERATION_1 acceleration
• -1 – search with pbio::Recognizer::verifyMatch in a single thread (independent of search_threads_count)
• FILES – file(s) obtained from the processing mode or by using the utility

Note: if a trial license is used, the value of [templates_count] cannot exceed 1000.

Example of launch from the bin directory:

./test_sdk \
    --mode search_speed_test \
    --dll_path ../lib/libfacerec.so \
    --sdk_config_dir ../conf/facerec \
    --recognizer_config method6v7_recognizer.xml \
    templates_6v7.bin

The result of the search speed test is a message with test results.

Utilities

Template Generator

A utility for creating random templates.

Launch parameters:

• recognizer_version – recognizer version (select one of [11v1000, 10v30, 10v100, 10v1000, 9v30, 9v300, 9v1000, 8v7, 8v6, 7v7, 7v6, 7v3, 7v2, 7, 6v7, 6v6, 6v5, 6v4, 6v3, 6v2, 6])
• templates_count – number of generated templates
• result_file – a result binary file, which contains the generated random templates

Example of launch:

./template_generator \
    6v7 \
    100000 \
    random_templates.bin

The template generator result is a binary file, which contains the generated random templates of the same format as in the processing mode.

Source code: examples/cpp/test_sdk

ROC-curve construction

A utility for constructing a ROC curve from a complete set of points.

WARNING

• Python 3 required to run the app
• Before running the program, you need to install the matplotlib package:

  pip3 install matplotlib

Launch parameters:

• rocs_folder - path to the folder where the files with the calculated set of points for the ROC curve are stored
• roc_points_files – names of files for which it is necessary to build a curve (it is possible to specify several files). The file name will be displayed as the name of the ROC curve on the graph

Run example:

python3 ./draw_roc_curves.py\
    ./base_roc \
    roc11_11v1000.txt \
    roc11_10v100.txt \
    roc11_10v30.txt \
    roc11_9v300.txt

Source code: examples/python/draw_roc_curves.py