FaceMe® eKYC & Fintech | System Requirements | CyberLink
FaceMe®

FaceMe® eKYC & Fintech

Facial Recognition Solutions for the Banking,
Financial Services and Insurance (BFSI) Industry

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

FaceMe® Technology

Features List
Face Detection
Yes
Face Recognition
Yes
Face Attributes Recognition
Gender, Age, Emotion, Pose
Real-time Video Support
Yes
Emotion Detection
Happy, Sad, Angry, Surprised, Neutral
Images Pre-Processing
Lighting Enhancement, Edge Sharpening, Upsampling
Anti-Spoofing with 3D-Cameras
Yes
Anti-Spoofing with 2D-Cameras
Yes(with or without user interactions)
Anti-Spoofing with Voice
Voice recognition / Speaker verification
Chipsets (CPU/GPU/VPU)
Intel®64, ARM64™, NVIDIA® GPU, NVIDIA® Jetson, Intel®Movidius™ VPU, MediaTek i350 APU, Qualcomm SoC(GPU), NXP i.MX8
Programming Interface
C++, HTTP, C#, Perl
AI Inference Engines
Intel® OpenVINO™, NVIDIA® TensorRT / CUDA, Qualcomm SNPE, MediaTeK NeuroPilot, CoreML
Against Identity Fraud
Yes
e-Passport Support (NFC)
Yes

System Requirements

For Server

Ultra High Precision Model (UH)
Very High Precision Model (VH)
High Precision Model (H)
CPU
Intel® Xeon® Processor E3 Family with 4 cores
(E5 Family with 8 cores recommended)
GPU
NVIDIA® Quadro RTX 4000 or
NVIDIA® Tesla T4
—
Memory
4GB (16GB recommended)
OS
Windows 10, Windows Server 2019, Ubuntu 16/18

For Mobile Devices

Android ARM64
iOS
CPU
Snapdragon 625
(835 recommended)
Apple A8
(A10 recommended)
Memory
1 GB
(1.5 GB recommended)
1 GB
(1.5 GB recommended)
OS
Android 5.0
iOS 13
Runtime Memory
500 MB
500 MB

Performance Numbers

For Server

CPU
AMD EPYC 7502 32-Core
GPU/VPU
NVIDIA® RTX A6000
FaceMe® Extraction Model
UH
VH
Execution Time (ms) Detection + Extraction
3.9 ms
2.4 ms
Frames per second (fps)
256.4 fps
416.7 fps
Face Match Time (ms)
0.0018 ms
0.0017 ms
Memory (GB)
3.9 GB
3.7 GB

* 1080p images, 1 face per image

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

* 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 5.4 is used

For Mobile Devices

Soc
Snapdragon 845
A12X
Device
Google Pixel 3
iPad Pro 12.9" 2018
FaceMe® Extraction Model
VH
H
VH
H
Execution Time (ms)
Detection + Extraction
55.1 ms
40.9 ms
29 ms
22 ms
Frames per second (fps)
18.1 fps
24.4 fps
34.5 fps
45.5 fps
Face Match Time (ms)
0.0006 ms
0.0006 ms
0.0025 ms
0.0009 ms
Soc
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
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

* Tested with Goggle Pixel3 and iPad Pro

* 720p images, 1 face per image

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

* FaceMe Android 5.3, FaceMe iOS 3.12 are used

For further information on CyberLink FaceMe® engine

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