FaceMe® AI Facial Recognition Engine | Tech Specs | CyberLink
FaceMe®

FaceMe®

The World’s Top AI Facial Recognition Engine

Top 10 in both NIST FRVT 1:1 & 1:N
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Overview
System Requirements

FaceMe® Accuracy from NIST

National Institute of Standards and Technology (NIST)

Match Type
True Acceptance Rate
Higher is Better
False Acceptance Rate
Lower is Better
1:1 VISA
99.73%
0.000001 (1E-6)
1:1 VISA Border
99.60%
0.000001 (1E-6)
1:1 Mugshot (>12 years)
99.54%
0.00001 (1E-5)
1:1 Wild
97%
0.00001 (1E-5)
1:N VISA Border (1.6M DB)
99.73%
N/A
Investigation Mode

* VISA images are photos taken from the passport. The mean interocular distance (IOD) in the VISA images are 69 pixels.

* Wild images are non-constraint, photojournalism-style photos. Resolution varies widely. A wide yaw and pitch angle may be present.

* VISA Border are comparing VISA image to WebCam images.

FaceMe® Accuracy

FaceMe® Model
Ultra High Precision
(UH)
Very High Precision
(VH)
High Precision Model
(H)
Recommended Platform
Server, Workstation
PC, Premium Mobile
Mobile, Light-weight IoT/AIoT Devices
True Acceptance Rate
(@FAR 1E-4)
99.05%
98.95%
98.35%
True Acceptance Rate
(@FAR 1E-5)
99.05%
98.95%
97.03%
True Acceptance Rate
(@FAR 1E-6)
98.89%
97.99%
94.98%
Model Size (MB)
300 MB
17 MB
6.7 MB
Template Size (KB)
5 KB
3 KB
3 KB

Performance Numbers

For Workstation or Server

CPU
Intel Core i7-12700K, 3.61GHz
GPU
NVIDIA® RTX A2000
FaceMe® Extraction Model
UH6
VH6
Frames per second (fps)
186.2 fps
543.5 fps
Face Match Time (ms)
0.0008 ms
0.0008 ms
Memory Usage (GB)
4.5 GB
3.9 GB

* 1080p images, 1 face per image

* Face Match Time: Time taken to match two templates. For example, given a template extracted from a face image, to identify who is this person in a 1,000 people database takes 1,000 times matching

* FaceMe® - Windows 6.9 is used

Server
Amazon EC2 G4
g4dn.2xlarge
CPU
vCPU x8
GPU
NVIDIA T4 x1
FaceMe® Extraction Model
UH6
VH6
Frames per second (fps)
235.8 fps
390.9 fps
Face Match Time (ms)
0.0032 ms
0.0016 ms
Memory Usage (GB)
4.5 GB
4.5 GB

* 1080p images, 1 face per image. Each time batch process 16 images​

* Face Match Time means Time taken to match two templates. For example, given a template extracted from a face image, to identify who is this person in a 1,000 people database takes 1,000 times matching​

* Ubuntu 20 x64 is used

For Industrial PC

CPU
Intel® Celeron® G4920
Core i7-7700K
FaceMe® Extraction Model
VH
H
VH
H
Execution Time (ms)
Detection + Extraction
41.4 ms
24.9 ms
10.1 ms
8.1 ms
Frames per second (fps)
24.2 fps
40.2 fps
99.0 fps
123.5 fps
Face Match Time (ms)
0.0016 ms
0.0016 ms
0.0011 ms
0.0011 ms
CPU
Intel® Celeron® G4920
FaceMe® Extraction Model
VH
H
Execution Time (ms)
Detection + Extraction
41.4 ms
24.9 ms
Frames per second (fps)
24.2 fps
40.2 fps
Face Match Time (ms)
0.0016 ms
0.0016 ms
CPU
Intel® Core i7-7700K
FaceMe® Extraction Model
VH
H
Execution Time (ms)
Detection + Extraction
10.1 ms
8.1 ms
Frames per second (fps)
99.0 fps
123.5 fps
Face Match Time (ms)
0.0011 ms
0.0011 ms

* 1080p images, 1 face per image

* FaceMe® SDK - Windows 5.2 is used

* The "High Precision (DNN)" model is used for Face Detection

* For VPU case, test is done by running 960 images concurrently using Batch mode and measure the average time per image using FaceMe SDK 3.15

For Mobile Devices & IoT/ AIoT Devices

Soc
Qualcomm Snapdragon 845
Qualcomm Snapdragon 660 (GPU)
MediaTeK i350 (APU)
Apple A12X
Device
Google Pixel 3
Advantech MOD Q200
MediaTeK i350 Dev Kit
iPad Pro 12.9” 2018
FaceMe® Extraction Model
VH
H
VH
H
VH
H
VH
H
Execution Time (ms)
Detection + Extraction
55.1 ms
40.9 ms
62.3 ms
37.7 ms
123.2 ms
54.2 ms
29 ms
22 ms
Frames per second (fps)
18.1 fps
24.4 fps
16.0 fps
26.5 fps
8.1 fps
18.4 fps
34.5 fps
45.5 fps
Face Match Time (ms)
0.0006 ms
0.0006 ms
0.0010 ms
0.0010 ms
0.0035 ms
0.0035 ms
0.0025 ms
0.0009 ms
Soc
Qualcomm Snapdragon 845
Device
Google Pixel 3
FaceMe® Extraction Model
VH
H
Execution Time (ms) Detection + Extraction
55.1 ms
40.9 ms
Frames per second (fps)
18.1 fps
24.4 fps
Face Match Time (ms)
0.0006 ms
0.0006 ms
Soc
Qualcomm Snapdragon 660 (GPU)
Device
Advantech MOD Q200
FaceMe® Extraction Model
VH
H
Execution Time (ms) Detection + Extraction
62.3 ms
37.7 ms
Frames per second (fps)
16.0 fps
26.5 fps
Face Match Time (ms)
0.0010 ms
0.0010 ms
Soc
MediaTeK i350 (APU)
Device
MediaTeK i350 Dev Kit
FaceMe® Extraction Model
VH
H
Execution Time (ms) Detection + Extraction
123.2 ms
54.2 ms
Frames per second (fps)
8.1 fps
18.4 fps
Face Match Time (ms)
0.0035 ms
0.0035 ms
Soc
A12X
Device
iPad Pro 12.9” 2018
FaceMe® Extraction Model
VH
H
Execution Time (ms) Detection + Extraction
29 ms
22 ms
Frames per second (fps)
34.5 fps
45.5 fps
Face Match Time (ms)
0.0025 ms
0.0009 ms

* 720p images, 1 face per image

FaceMe® Technology

Features List
Face Related Features​
Detection, Extraction, Matching, Searching​
Occlusion Detection, Emotion Detection
Landmark Detection, Medical Mask Detection​
Person Related Features
Detection, Counting​
Face Attributes Recognition​
Gender, Age, Emotion, Pose​
Input Mode Support​
Image, Video​
Video Format *1​
H.264 (AVC), H.265 (HEVC)​
Emotion Detection​
Happy, Sad, Angry, Surprised, Neutral​
Anti-spoofing​
3D Depth Anti-spoofing
IR+RGB Anti-spoofing
​ 2D Camera Anti-spoofing (with or without random interactions)​
Chipsets​
Intel x64,
ARM64,
NVIDIA GPU, NVIDIA Jetson,
​ MediaTek SoC (i350, i500),
Qualcomm SoC (w/ GPU or DSP),
NXP i.MX8​
Database Support​
FaceMe SDK Built-in,
Microsoft SQL, MySQL, Maria DB​
Operating Systems​
Windows, Windows Server, Android, iOS
Ubuntu, Red Hat, CentOS, JetPack, Yocto*2​
AI Inference Engines​
Intel® OpenVINO™,
NVIDIA® TensorRT / CUDA,
​ Qualcomm SNPE,
MediaTeK NeuroPilot,
Apple CoreML​

* 1. Hardware decoder is required to support video formats​

* 2. Yocto support is for specific OEM devices

Images/ Cameras Conditions

Minimum
Recommended
Face Pose – Yaw
< 60°
< 45°
Face Pose – Pitch
< 50°
< 30°
Face Pose – Roll
< 60°
< 45°
Lighting
450 lux
550 lux
Face Size
(pixels between eyes)
36 pixels
50 pixels
Face Enrollment
1 image
5 images - Straight, Left/Right (15°~45°), Up (5°~30°), Down (15°~30°)

Anti-spoofing using 3D-Depth Camera

Camera Types​
3D Depth​
IR+RGB
2D Camera
True Acceptance Rates​​
100%​
99.97%
98.22%
True Rejection Rates​
100%​
100%
98.07%
Compatible Devices​​
Himax SH430UH
iPhone X, and iPad Pro devices
Intel RealSense D415
Orbbec Astra Pro 3D
eYs3D EX8053 ​​
Union Ubio-X Face Premium series
Fangtec DCEA23-HK1-6R
​ TaisenTech H018-7168-V2
Supports almost all Android/iOS smart phones or tablets

* For 2D Camera column, The FaceMe Android/iOS is used with default settings.

* Tested with FaceMe 6.8

Fast Search Algorithm for an Enormous Database

People in Database
Time to Search in Database (ms)
One-by-one Match
Fast Search
1 Million People
1,115 ms
7 ms
6.67 Million People
7,437 ms
9 ms

* Tested on an i7-7700K with 64GB memory PC, Windows 10 Pro.​

* A database of 30 million faces would require 128 GB of memory.

Age, Gender, and Emotion Detection

Detection Attributes
Accuracy Rate
Gender
98%
Emotion
Up to 86%
Age
5.8 y/o (Mean Average Error)

* Tested with 68,000 images.

Contact
Our Sales Team
Contact the FaceMe Team