Develop A Face Recognition System on ARM Board Using OpenCV

WU JiaOpenCV China Team

Outline

• Face recognition in brief•Build a FR system on ARM board using OpenCV•Assignment

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Face recognition is to identify or verify a person from an image or a video frame.

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Face

Reco

gniti

on

A

B

C

Who is this person, 1:N Is this person X, 1:1

Face

Reco

gniti

on

Is A

Not A

Not A

Face Recognition Pipeline

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input images face detection face alignment

calculate similarity

face representation

same person

different person

Face Detection • Traditional methods

Viola-Jones face detector, DPM etc.

• Deep learning based

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Hang Du et al., The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advancesthe development of representa0ve face detec0on methods

multi-stagesingle-stage

anchor-basedanchor-freeother

Face Alignment To obtain a canonical layout of the unconstrained face.

• Landmark-based methods• Landmark-free methods• Face frontalization

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coordinate regressionheatmap regression3D model fittinglandmark-freeface frontalization

Hang Du et al., The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

Face Representation To extract discrimina6ve features from aligned face images, which is key to a face recogni6on system.• Tradi:onal methods

Eigenface, Gabor, LBP etc.

• Deep learning based

6Hang Du et al., The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

classificationfeature embedding

semi-supervisedhybrid

Outline

• Face recogniCon in brief•Build a FR system on ARM board using OpenCV•Assignment

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8

usb cameraARM dev board

face detection face preprocessing

featuredatabase

face detec<on face preprocessing

feature extraction

Registration Image

Camera Image result

Registration

Recognition

feature extrac<on

calculatesimilarity

Face Recognition System Architecture

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EAIDK-310

EXT_IO RECOVER RK3228H MASKROM

USB2.0X2

USB3.0X1USB2.0X1

RJ45Ethernet

WIFI/BT

LPDDR3

RESET

DC5V IN HDMI OUT SPEAKER

OpenCV DNN Module

• Inference framework• Compatible to many popular deep learning frameworks

• Run on multiple devices: CPU, GPU, VPU, FPGA • Run on different OS: Linux, Windows, Android, MacOS• Backend framework to leverage different acceleration libs

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Lightness

Convenience

UniversalityAccelerated

well tested networks

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Image Classification Object DetectionSemantic

SegmentationPose

EstimationImage Processing Person

Identification

Text Detection and

Recognition

Depth Estimation

CaffeAlexNetGoogLeNetVGGResNetSqueezeNetDenseNetShuffleNet

TensorFlowInceptionMobileNetEfficientNet

Darknetdarknet19

ONNXAlexNetGoogleNetCaffeNetRCNN_ILSVRC13ZFNet512VGG16VGG16_bnResNet-18v1ResNet-50v1CNN MnistMobileNetv2LResNet100E-IREmotion FERPlusSqueezenetDenseNet121Inception v1, v2Shufflenet

CaffeSSD VGGMobileNet-SSDFaster-RCNNR-FCNOpenCV Face Detector

TensorFlowSSDFaster-RCNNMask-RCNNEASTEfficientDet

DarknetYOLOv2tiny YOLO YOLOv3tiny YOLOv3YOLOv4tiny YOLOv4

ONNXTinyYolov2PyTorchYOLOv3PyTorchYOLOv3-tinyPyTorch SSD VGG

CaffeFCN

TorchENet

ONNXResNet101_DUC_HDC

TensorFlowDeepLab

PytorchUNetDeepLabV3FPNUNetPlusBiSeNet

CaffeOpenPose

PyTrochAlphaPose

CaffeColorization

TorchFast-Neural-Style

ONNXStyle Transfer

TorchOpenFace

PyTorchTorchreid

TensorFlowMoibleFaceNet

PyTorchEasyOCRCRNN

PyTorchMonodepth2

For more detail: https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV

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https://www.learnopencv.com/cpu-performance-comparison-of-opencv-and-other-deep-learning-frameworks/

85.4 182.8

3008.3

53.9 70.1

1184.7

0500

100015002000250030003500

Squeeze Net 1.1 MobileNet SSD 1.0 VGG16

Inference time(ms) @Cortex-A72x2

OpenCV OpenCV+Tengine-lite

Tengine: • edge AI inference framework. • Surports CPU, GPU, DSP, NPU. • Surports TensorFlow, Caffe, MxNet,

PyThorch, MegEngine, DarkNet, ONNX, ncnn.

AWS t2.large instance (2 vCPUs and 8 GB of RAM, no GPU), Ubuntu 16.04 LTS

Speed comparison

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Easy-to-useload dl modelchoose accelerating backendset target device

inferencing

Now let’s try to build the system

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landmark detection

face detection

feature extractionOpenFace

model

libfacedetection

similarity l2 distance

Speed can reach 1000FPS.https://github.com/ShiqiYu/libfacedetection

face detection face preprocessing

feature extraction

Input Image

face and landmark detection

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face alignment

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feature extraction

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calculate similarity

Outline

• Face recognition in brief•Build a FR system on ARM board using OpenCV•Assignment

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Assignment

A. Build a complete face recognition system on ARM board using OpenCV. Write a report in English about the system.

B. Build any other kind of biometric recognition system on ARM board using OpenCV. Write a report in English about the system.

• Submit to: jia.wu@opencv.org.cn• Deadline: 23:59 Jan. 27, 2021• Top 3 teams will be awarded the advanced EAIDK-610 Kit.

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Issues

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• Training‒ use deep learning frameworks

•Registration

•UI

face detec<on face preprocessing

featuredatabase

Registration Image

feature extraction

Thank You!

www.opencv.org.cn