Uploaded byJamie (Taka) Wang
intelligent access control system - 20180628
该文档讨论了一种智能门禁控制系统的设计，旨在通过面部识别技术提高员工进出厂区的效率和安全。系统通过实时统计进出时间并与政府法规对接，使用二维码和面部识别技术进行身份验证，解决了传统门禁卡相关的诸多问题。此外，文档还探讨了技术架构、风险管理和市场现有解决方案的比较。
intelligent access control system - 20180628
- 1. Intelligent Access ControlSystem Idea Discussion Taka Wang 2018.06.28
- 2. Problem Statement /Executive Summary Problem Statement • 人 員進出廠區，需要配發 門 禁卡，有磁卡感應不良、毀損、回收或忘記 攜帶的問題。 • 出勤與加班時間統計，無法即時有效的掌握，與其他 Enterprise System 整合度不 高 。 Executive Summary • 一 般員 工 進出廠區，透過刷臉微笑，紀錄進出時間。 • 後台系統即時統計進出廠時間，配合政府法規，給予加班統計，超時警 示 。 • 發放 一 般員 工 QR Code 與 一 次性 QR Code，輔助給予通 行 權限。 • 紀錄、保存通過系統時的驗證圖像，作為 日 後追蹤與查驗的數位證據。
- 3. Technical Infrastructure SBC Panel SBC Panel Public Cloud •Inference • Mgmt. SBC Panel SBC Panel InwinStack Cloud • Inference • Mgmt. In-house Server SOHO/SMB Server • Inference • Mgmt. SBC Panel SBC Panel SBC Panel SBC Panel • Mgmt. • Inference Internet Internet Intranet Intranet
- 4. Physical Layout ofthe prototype IR Proximity Sensor Main Camera Second Camera WIFI Power cord Touch Panel
- 5. Software Solution Sketch Events Delay HMI Mgmt.Portal UIs Processes Rules Data DBs & Applications User Interface System Admin Employee Continuous working days Overtime Handle Overtime Handle Delay Rest Day Invader Handle Invader Employee pro fi les Historical Records Face Recognition QR Code Decode
- 6. End User HMIMockups Current Time: 2018 Jun 28 10:02 AM Employee Name: Elizabeth Lin QR Code Current Time: Employee Name: Face ID Face ID Mode QR Code Mode
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- 12. Make or BuyDecision ‣ Public Cloud API (Pay by usage) • Pros: 開發整合速度快 • Cons: 關鍵技術無法掌握 ‣ O ff -the-Shelf (COTS) solutions • Pros: 無須投入 AI R&D 人 力 • Cons: 前期成本 高 ，沒有擴充性 ‣ Make • Pros: 掌握關鍵技術 • Cons: 前期開發有不確定的風險， 長期需要逐漸增加 R&D 能量
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- 15. Risk Management forMVP Area Goal Risk Level Situation Hardware Design 用 現成的硬體組合出 prototype Low 需要採購與測試 SBC 效能是 否 足 夠 Infrastructure Integration 與辦公室現有 門 禁系統整合 Medium 確定電源供應插孔與網路狀 況，另需整合 門 禁繼電器 Face Recognition 辨識已知使 用 者的微笑 Low 有 Cloud API, commercial SDK 與 open source 方 案 Mgmt. Backend 管理與統計 門 禁出入紀錄 High 需要了解考勤基本規則並開 發有擴展性的後台 Mgmt. Portal & HMI 良好的前後台使 用 者體驗 Very High 目 前公司沒有這 方 面 的專職 人 才 SaaS Operations Management (aka. DevOps) 維運不中斷的雲端服務 Very High 目 前公司沒有這 方 面 的專職 人 才
- 16. R&D Resource Estimationfor MVP ‣ Design & Discussion: 1~2 週 ‣ Hardware Assembly: 1~2 ( 人 /週) ‣ Face Recognition R&D : 4 ( 人 /週) ‣ User HMI: 4 ( 人 /週) ‣ Integration: 2 ( 人 /週) ‣ Portal Backend: 8~16 ( 人 /週) ‣ Portal Frontend: 8~16 ( 人 /週) ‣ Infrastructure integration: 1~2 ( 人 /週)
- 17. Face Recognition indepth
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- 19. SERVICEFEATURE DETECTION LANDMARKSRECOGNITION AGE/GENDER EMOTION HEAD POSE OTHER CLASSIFIERS BETAFACE API Yes 123 Yes Yes Smile/Neutral No Facial hair, Glasses FACE++ Yes 83 or 106 Yes Yes 7 classes Yes Face quality Blur Eye status Ethnicity Beauty AMAZON AWS REKOGNITION Yes 23 Yes Yes 3 Classes + Smile intensity Yes Beard Mustache Sunglasses EyesOpen Mouth Open MICROSOFT Yes 27 Yes Yes Smile Intensity Yes Facial hair, glasses GOOGLE CLOUD PLATFORM Yes No No No Smile/Neutral No Headwear IBM WATSON FACE Yes No Yes Yes No No None ANIMETRICS FACER API Yes 24 Yes Yes No Yes None KAIROS API Yes 49 Yes Yes 6 classes Yes Ethnicity Eye status MEERKAT FR Yes 68 Yes No No Yes None Cloud API Reference
- 20. SERVICEFEATURE PLATFORMS DETECTIONLANDMARKS RECOGNITION AGE/GENDER EMOTION HEAD POSE OTHER CLASSIFIERS BETAFACE SDK Win/Lin Yes 123 Yes Yes Smile/Neutral No Facial hair, Glasses COGNITEC VACS Win/Lin/ARM Yes unknown Yes Yes No Yes Glasses Eye status Lighting, Color Image and face geometry Full-frontal image check as LUXAND FACESDK Win/Lin/ARM Yes 70 Yes Yes Smile/Neutral Yes Eye status NEUROTECH VERILOOK SDK Win/Lin/ARM Yes No Yes No No No None SIGHTCORP INSIGHT SDK Win/Lin/ARM Yes unknown No Yes 7 classes Yes None ANIMETRICS FACER API Win/Lin Yes 24 Yes Yes No Yes None VISAGE SDK Win/Lin/ARM Yes 68 Yes Yes 6 classes Yes Eye status, Gaze tracking AFFECTIVA SDK Win/Lin/ARM Yes No FALSE Yes 7 classes Yes Ethnicity DEEPSIGHT SDK Win/Lin Yes 68 Yes Yes soon soon None Commercial SDKs Reference
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- 22. FaceNet (CVPR2015) Full paper Stateof the art
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- 24. Training vs. Inference LargeN Forward Backward “car” ? Error TRANING Reference: Discover the Difference Between Deep Learning Training and Inference
- 25. Training vs. Inference LargeN Forward Backward “car” ? Error TRANING Smaller, varied N Forward “car” INFERENCE Reference: Discover the Difference Between Deep Learning Training and Inference
- 26. Recall binary classi fi cation (binary attributes) multiclassclassi fi cation (categorical attributes) Regression (numeric attributes)
- 27. 500k Labeled TrainingImages Convolutional Neural Network (Feature Extractor) FaceNet’s Inception Model Face Representation FaceNet’s Triplet Loss Trained Neural Network (128 dimensions) Training (Only done once) (Feature Extractor) CASIA-WebFace - 10,575 individuals - 494,414 images FaceScrub - 530 individuals - 106,863 images Face Detection dlib’s pre-trained detector Preprocessing Each face Convolutional Neural Network (Feature Extractor) FaceNet’s Inception Model Face Representation Classi fi cation (128 dimensions) sklearn’s SVM (faces) Af fi ne Transformation Openface implementation anchor anchor negative negative positive positive Learning
- 28. Relevant Application Home Surveillancewith Facial Recognition Check the architecture!!
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- 32. Shuttle BR06 硬體產品 ATurnkey Solution for Face Recognition Youtube
- 33. Shuttle BR06 硬體產品 ATurnkey Solution for Face Recognition Youtube
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- 35. The Elderly DayCare Center 蘆洲老 人 日 間護理中 心 (Check-in) 板橋與新莊老 人 日 間護理中 心 (防 止 老 人 癡呆長者離開) Source
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