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市場調查報告書

付款用生物辨識 - 付款方式安全措施的個人化:市場·技術分析,引進策略,未來預測 (2015∼2020年)

Biometrics for Payments - Payment Security Gets Personal; Market and Technology Analysis, Adoption Strategies & Forecasts 2015-2020

出版商 Goode Intelligence 商品編碼 342253
出版日期 內容資訊 英文 243 Pages
商品交期: 最快1-2個工作天內
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付款用生物辨識 - 付款方式安全措施的個人化:市場·技術分析,引進策略,未來預測 (2015∼2020年) Biometrics for Payments - Payment Security Gets Personal; Market and Technology Analysis, Adoption Strategies & Forecasts 2015-2020
出版日期: 2015年10月02日 內容資訊: 英文 243 Pages
簡介

隨著消費者主導型態的生物辨識安全技術的普及,也帶給了生物辨識·供應商莫大的市場機會。付款用生物辦識的引進,成了該產業巨大成長促進要素,預測2020年使用生物辦識的交易總額將達到5兆6000億美元,生物辨識相關收益額將為556億美元。

本報告提供付款方式用生物辨識 (生物識別) 技術·市場現況與未來展望相關分析,提供您各種付款方式的情況,技術引進主要的促進·阻礙因素,各種生物辨識技術的特徵和主要供應商,全球·各地區付款用生物辨識市場未來趨勢 (用戶數·付款額,今後6年份) 等調查評估。

序論

關於Goode Intelligence

著者

分析方法

分析地區定義

摘要整理

第1章 市場·技術分析

  • 簡介
  • 生物辨識技術的概要
  • 生物辨識定義
  • 生物辨識的功能做方法
  • 生物學認證
  • 付款方式上生物辨識的活用方法
  • 推動及阻礙市場要素
  • 金融服務·付款用生物辨識的法規·技術規格
    • 簡介
    • 法規
    • 各國政府的管理體制
    • 產業內的法規/指南
    • 技術規格
  • 各種技術的市場的引進
    • 市場的引進與分析
    • 與實體商店的付款用生物辨識:POS終端的一體化
    • 電子商務的付款的生物辨識
    • 行動電子商務的付款的生物辨識
    • 穿戴式設備的付款 (w貿易) 的生物辨識
    • 比特幣·Blockchain付款上的生物辨識
    • ATM (現金付款) 的生物辨識
    • 生物辨識技術:分析 (概要,優點·缺點) 和供應商
      • 簡介
      • 行動認證
      • 眼 (虹膜·視網膜) 靜脈
      • 臉認證
      • 指紋認證
      • 透過心臟跳動的認證
      • 語音辨識
      • 指尖靜脈認證
      • 手掌靜脈認證

第2章 未來預測

  • 預測手法與前提條件
  • 付款方式用生物辨識市場預測
    • 簡介
    • 用戶數的預測
      • 技術方式別
        • 指紋認證
        • 語音辨識
        • 眼 (虹膜·視網膜) 靜脈
        • 行動認證
        • 指尖靜脈認證
        • 手掌靜脈認證
        • 心臟 (心電圖) 認證
        • 臉認證
        • 複合認證
      • 各付款方式
        • 零售商店 (POS終端一體型)
        • 零售商店 (生物辨識支付卡)
        • 電子商務
        • 行動付款
        • 穿戴式·付款
        • 比特幣
        • 由於ATM的現金付款
    • 市場收益額的預測 (技術方式別)
      • 前提條件
      • 指紋認證
      • 語音辨識
      • 眼 (虹膜·視網膜) 靜脈
      • 行動認證
      • 指尖靜脈認證
      • 手掌靜脈認證
      • 心臟 (心電圖) 認證
      • 臉認證
      • 複合認證
    • 付款數量·結算金額
      • 前提條件
      • 付款數量
      • 結算金額 (總額)

附錄

目錄
Product Code: GICAR08a

Biometrics for Payments - Payment Security Gets Personal; Market & Technology Analysis, Adoption Strategies & Forecasts 2015-2020 is a 264 page analyst report that provides detailed analysis of the adoption of biometrics for payments.

This comprehensive report includes a review of current global adoption, market analysis including key drivers and barriers for adoption, interviews with leading stakeholders, technology analysis with review of key biometric technologies and profiles of companies supplying biometric systems for the payments industry plus forecasts (regional and global) for users and revenue within the six-year period 2015 to 2020.

Alan Goode, author of the report and founder of Goode Intelligence said "Biometric vendors are experiencing tremendous growth on the back of the escalation of consumer-led adoption of biometric security. The adoption for payment purposes is a major contributor to this growth and Goode Intelligence forecasts that by 2020 it will contribute US$5.6 billion in revenue from $5.6 trillion worth of payments for companies involved in delivering biometric systems to the payments industry."

Coverage

The report identifies the major trends shaping this industry and includes analysis of how biometrics is reshaping the payments industry. The following payment types and technologies are covered:

  • Physical Retail Store - Point-of-Sale (POS) terminal integration
  • eCommerce (web-based payments)
  • Mobile Payments
  • Wearable Payments
  • Bitcoin
  • ATM (cash and Bitcoin)

Goode Intelligence has identified eight biometric technologies that are reshaping the payment security market:

  • Behavioral
  • Eye (Iris and Eye-Vein)
  • Face
  • Fingerprint
  • Finger-Vein
  • Heartbeat
  • Palm-Vein
  • Voice

The report includes an analysis of all eight biometric technologies with a review of the active vendors offering these biometric solutions.

This report is aimed at technology vendors and service providers involved in providing biometric systems for payments and payment service providers wanting to understand the reasons for deploying and what biometric solutions they should implement. The report is also aimed at management and technology consultants, hardware ODMs looking to support biometrics and investors looking to take advantage of this growing industry.

Companies referenced in this report include: Access Softek, Alipay, Agnitio, Apple, ARM, Banco Bradesco, Bank BGZ, Bank BPH, Banco Santander, Bank of Lanzhou, Bank Muscat, Bank of China, Bank of Korea, Barclays, BehavioSec, BioCatch, BIO-key, Biometrics Signature ID, British Bankers Association, Cash, Cairo Amman Bank, Cardtech, Cifas, Cirrus Logic, Cryptovision, Daon, DARPA, Dermalog, Diamond Fortress, Diebold, EarlyWarning, Edgar, Dunn & Co., EMVCo, Encap Security, Ernst & Young, ECB, eyeLock, EyeVerify, Facebanx, FFIEC, FCA, FFA, Fingerprint Cards, Fotonation, Fujitsu, Garanti Bank, Gemalto, Getin Bank, Google, Hitachi, Hoyos Labs, HYPR Corp., Idair, IDEX, IEEE, ING Bank, IrisGuard, Isbank, ITCARD, KeyLemon, Lamassu, Lumidigm, MasterCard, Microsoft, Morpho, M2SYS, Natwest Bank, NICE, NIST, NCR, NEXT Biometrics, Nok Nok Labs, NTT DoCoMo, Nuance, Nymi, OCBC, OCC, OKI, PayPal, Pindrop, Planet Cash, Qualcomm, RBS, Robocoin, Royal Bank of Canada, Royal Oman Police, RSA Security, Salesforce, Samsung, SayPay, Smartmetric, St Georges Bank, Sonavation, Synaptics, Tangerine Bank, Tencent, The FIDO Alliance, The Natural Security Alliance, Trustonic, Turkiye IS Bankasi, USAA, US Federal Reserve, ValidSoft, Verint, VISA, VoiceTrust, VoiceVault, VOXX International, Westpac, Wincor Nixdorf, ZTE, Zwipe.

Table of Contents

Forward

Goode Intelligence

Author

Goode Intelligence Methodology

Regional Definitions

ES Executive Summary

  • ES 1 Report Scope
  • ES 2 Market and Technology Analysis
    • ES 2.1 An Introduction to Biometric Technologies
      • ES 2.1.1 Biometrics Definition
      • ES 2.1.2 Biometric Authentication
    • ES 2.2 How is Biometrics used for Payments
      • ES 2.2.1 How to Measure Biometric Technology? A Guide to Choosing the Right Biometric System
    • ES 2.3 Market Drivers and Barriers
      • ES 2.3.1 Market Drivers
      • ES 2.3.2 Market Barriers
    • ES 2.4 Financial Services & Payments Biometric Regulation and Technology Standards
      • ES 2.4.1 Regulation
      • ES 2.4.2 Industry Regulation / Guidelines
      • ES 2.4.3 Technology Standards
    • ES 2.5 Technology and Market Adoption
      • ES 2.5.1 Market Adoption and Analysis
        • ES 2.5.1.1 So, Why Biometrics Now?
        • ES 2.5.1.2 Biometrics For Physical Store Payments
        • ES 2.5.1.3 Biometrics for eCommerce Payments
        • ES 2.5.1.4 Biometrics for mCommerce Payments - Payment Apps
        • ES 2.5.1.5 Biometrics for Wearable Payments (wCommerce)
        • ES 2.5.1.6 Biometrics for Bitcoin and Blockchain Payments
        • ES 2.5.1.7 Biometrics for ATMs
    • ES 2.6 Biometric Technology - Analysis and Vendors
      • ES 2.6.1 Behavioral Biometrics
        • ES 2.6.1.1 Advantages and Disadvantages of Behavioral Biometrics
      • ES 2.6.2 Eye Biometrics
        • ES 2.6.2.1 Iris
          • ES 2.6.2.1.1 Advantages and Disadvantages of Iris Biometrics
        • ES 2.6.2.2 Eye-Vein
          • ES 2.6.2.2.1 Advantages and Disadvantages of Eye-Vein Biometrics
      • ES 2.6.3 Face Biometrics
        • ES 2.6.3.1 Advantages and Disadvantages of Face Biometrics
      • ES 2.6.4 Fingerprint Biometrics
        • ES 2.6.4.1 Advantages and Disadvantages of Fingerprint Biometrics
        • ES 2.6.4.5 Heart Biometrics
          • ES 2.6.4.5.1 Advantages and Disadvantages of Heart Biometrics
      • ES 2.6.6 Voice Biometrics
        • ES 2.6.6.1 Advantages and Disadvantages of Voice Biometrics
      • ES 2.6.7 Finger-Vein Biometrics
        • ES 2.6.7.1 Advantages and Disadvantages of Finger-Vein Biometrics
      • ES 2.6.8 Palm-Vein Biometrics
        • ES 2.6.8.1 Advantages and Disadvantages of Palm-Vein Biometrics
  • ES 3 Forecasts
    • ES 3.1 Methodology and Assumptions
    • ES 3.2 Biometric For Payments Forecasts
      • ES 3.2.1 Introduction
      • ES 3.2.2 Biometrics for Payments User Forecasts
        • ES 3.2.2.1 Biometrics for Payments User Forecasts - By Technology
        • ES 3.2.2.2 Biometrics for Payments User Forecasts - By Payment Type
      • ES 3.2.3 Biometrics for Payments Revenue Forecasts
        • ES 3.2.3.1 Biometrics for Payments Revenue Forecasts - By Technology
      • ES 3.2.4 Biometrics for Payment Transaction & Transaction Value Forecasts
        • ES 3.2.4.1 Assumptions on Transaction & Transaction Value Forecasts
        • ES 3.2.4.2 Biometric for Payments Transaction Forecasts - Total Transactions
        • ES 3.2.4.3 Biometrics for Payments Transaction Forecasts - Total Value ($)
  • ES 4 Conclusions

1 Market and Technology Analysis

  • 1.1 Introduction
  • 1.2 An Introduction to Biometric Technologies
  • 1.3 Biometrics Definition
  • 1.4 How Biometrics Works
  • 1.5 Biometric Authentication
  • 1.6 How is Biometrics used in Payments
  • 1.7 Market Drivers & Barriers
    • 1.7.1 Market Drivers
    • 1.7.2 Market Barriers
  • 1.8 Financial Services & Payments Biometric Regulation and Technology Standards
    • 1.8.1 Introduction
    • 1.8.2 Regulation
    • 1.8.3 State & Federal Regulation
      • 1.8.3.1 European Union (EU) Data Protection Act (DPA)
      • 1.8.3.2 European Union Payment Services Directive (PSD) II
    • 1.8.4 Industry Regulation / Guidelines
      • 1.8.4.1 PCI-DSS
      • 1.8.4.2 USA FFIEC
      • 1.8.4.3 EMV
      • 1.8.4.4 3D Secure
      • 1.8.4.5 Bank of Korea
      • 1.8.4.6 Bank of China
      • 1.8.4.7 Hong Kong - The Office of the Privacy Commissioner for Personal Data
    • 1.8.5 Technology Standards
      • 1.8.5.1 Introduction
      • 1.8.5.2 ISO
      • 1.8.5.3 ANSI
      • 1.8.5.4 UK CESG
      • 1.8.5.5 USA NIST
      • 1.8.5.6 The FIDO Alliance
      • 1.8.5.7 The Natural Security Alliance
      • 1.8.5.8 IEEE Biometric Open Protocol Standard (BOPS)
  • 1.9 Technology and Market Adoption
    • 1.9.1 Market Adoption and Analysis
      • 1.9.1.1 Biometrics for Payments
      • 1.9.1.1.2 So, Why Biometrics Now?
      • 1.9.1.1.3 The Move towards Multi-Modal Biometrics
    • 1.9.1.2 Biometrics for Physical Retail Store Payments - Point-of-Sale Terminal Integration
      • 1.9.1.2.1 Introduction
      • 1.9.1.2.2 Card-less Biometric Payment at the Point-of-Sale
      • 1.9.1.2.3 Match-on-Card Biometrics at the Point-of-Sale
    • 1.9.1.3 Biometrics for eCommerce Payments
      • 1.9.1.3.1 Introduction
      • 1.9.1.3.2 Continuous Verification using BehavioSec's BehavioWeb
      • 1.9.1.3.3 BioCatch Behavioral Biometrics reduces Fraud by Securing New Account Set-up and Reduces Vishing Fraud Exposure
      • 1.9.1.3.4 Using Biometrics to support Know Your Customer (KYC) and Anti-Money Laundering (AML) methods: Facebanx Biometric Identity Management Solutions
    • 1.9.1.4 Biometrics for mCommerce Payments - Mobile Payments
      • 1.9.1.4.1 Introduction
      • 1.9.1.4.2 Smart Mobile Device OEMs and Platforms
        • 1.9.1.4.2.1 Apple Pay
        • 1.9.1.4.2.2 Android Pay
        • 1.9.1.4.2.3 Samsung Pay
      • 1.9.1.4.3 Card Schemes
        • 1.9.1.4.3.1 Mastercard - Pay by Selfie
      • 1.9.1.4.4 eCommerce Payment Providers
        • 1.9.1.4.4.1 Alipay
        • 1.9.1.4.4.2 PayPal
      • 1.9.1.4.5 Mobile Network Operators - Direct Carrier Billing
        • 1.9.1.4.5.1 NTT DOCOMO
    • 1.9.1.5 Biometrics for Wearable Payments (wCommerce)
      • 1.9.1.5.1 Introduction
      • 1.9.1.5.2 The Nymi Band - Showcasing Biometric Wearable Payments
    • 1.9.1.6 Biometrics for Bitcoin and Blockchain Payments
      • 1.9.1.6.1 Introduction
      • 1.9.1.6.2 Using Biometrics to Secure Bitcoin & Other Digital Currency Platforms by answering the key question, "Am I Who I Say I Am" - HYPR Corp.
      • 1.9.1.6.3 Case - The Hardware Bitcoin Wallet Secured by Biometrics
      • 1.9.1.6.4 NYMI - The First Wearable Bitcoin Wallet Secured by Biometrics
    • 1.9.1.7 Biometrics for ATMs (Cash Payments)
      • 1.9.1.7.1 Introduction
      • 1.9.1.7.2 Cash
        • 1.9.1.7.2.1 Biometric Technology Integrated into ATM
          • 1.9.1.7.2.1.1 Planet Cash Biometric ATM Network in Poland - Card-Less Cash Withdrawal using Hitachi's Vein-ID Biometrics
          • 1.9.1.7.2.1.2 Brazilian Biometric ATMs Powered by Lumidigm's Multi-Spectral Imaging Fingerprint Sensors
          • 1.9.1.7.2.1.3 Banco Bradesco in Brazil Reduces ATM Fraud by Deploying Fujitsu's PalmSecure Palm-Vein Technology
        • 1.9.1.7.2.2 Biometric Technology Integrated into Separate Technology for use at ATM
          • 1.9.1.7.2.2.1 Leveraging Mobile-based Biometrics for ATM Cash Withdrawals - Hoyos Labs 1U ATM
        • 1.9.1.7.2.3 Biometric Bank and Payment Cards
          • 1.9.1.7.2.3.1 Integrated Sensor Biometric Cards
          • 1.9.1.7.2.3.2 How does the Technology Work?
          • 1.9.1.7.2.3.3 Match-on-Card Biometric Cards
        • 1.9.1.7.2.4 Bitcoin ATMs
          • 1.9.1.7.2.4.1 Introduction
          • 1.9.1.7.2.4.2 Robocoin deploys Biometrics to Bitcoin ATMs... then remove it
    • 1.9.2 Biometric Technology - Analysis and Vendors
      • 1.9.2.1 Introduction
      • 1.9.2.3 Behavioral Biometrics
        • 1.9.2.3.1 Introduction
        • 1.9.2.3.2 Technology Vendors and Service Providers
          • 1.9.2.3.2.1 BehavioSec
          • 1.9.2.3.2.2 Encap Security
          • 1.9.2.3.2.3 Biometric Signature ID
          • 1.9.2.3.2.4 BioCatch
        • 1.9.2.3.3 Advantages and Disadvantages of Behavioral Biometrics
      • 1.9.2.4 Eye (Ocular) Biometrics
        • 1.9.2.4.1 Introduction
        • 1.9.2.4.2 Iris
          • 1.9.2.4.2.1 Introduction
          • 1.9.2.4.2.2 Technology Vendors and Service Providers
            • 1.9.2.4.2.2.1 EyeLock
            • 1.9.2.4.2.2.2 Hoyos Labs
            • 1.9.2.4.2.2.3 Fotonation / Smart Sensors
          • 1.9.2.4.2.3 Advantages and Disadvantages of Iris Biometrics
        • 1.9.2.4.3 Eye-Vein
          • 1.9.2.4.3.1 Introduction
          • 1.9.2.4.3.2 Technology Vendors and Service Providers
            • 1.9.2.4.3.2.1 EyeVerify
          • 1.9.2.4.3.3 Advantages and Disadvantages of Eye Vein Biometrics
      • 1.9.2.5 Face
        • 1.9.2.5.1 Introduction
        • 1.9.2.5.2 Technology Vendors and Service Providers
          • 1.9.2.5.2.1 Facebanx
          • 1.9.2.5.2.2 KeyLemon
          • 1.9.2.5.2.3 Daon IdentityX
        • 1.9.2.5.3 Advantages and Disadvantages of Face Biometrics
      • 1.9.2.6 Fingerprint
        • 1.9.2.6.1 Introduction
        • 1.9.2.6.2 Technology Vendors and service Providers - Fingerprint Sensor Manufacturers and Authentication Integrators
          • 1.9.2.6.2.1 Fingerprint Cards
          • 1.9.2.6.2.2 IDEX
          • 1.9.2.6.2.3 Dermalog
          • 1.9.2.6.2.4 Lumidigm - HID Global
          • 1.9.2.6.2.5 Next Biometrics
          • 1.9.2.6.2.6 Sonavation
          • 1.9.2.6.2.7 Qualcomm
          • 1.9.2.6.2.8 Synaptics Biometric Products Division
          • 1.9.2.6.2.9 Nok Nok Labs
          • 1.9.2.6.2.10 BIO-key
          • 1.9.2.6.2.11 Zwipe
        • 1.9.2.6.3 Camera-based Fingerprint Biometrics - Touchless
          • 1.9.2.6.3.1 Technology Vendors and Service Providers - Touchless Fingerprint Biometrics
            • 1.9.2.6.3.1.1 Diamond Fortress
            • 1.9.2.6.3.1.1 IDair
        • 1.9.2.6.4 Advantages and Disadvantages of Fingerprint Biometrics
      • 1.9.2.7 Heart
        • 1.9.2.7.1 Introduction
        • 1.9.2.7.2 Technology Vendors and Service Providers
        • 1.9.2.7.2.1 Nymi
        • 1.9.2.7.3 Advantages and Disadvantages of Heart Biometrics
      • 1.9.2.8 Voice
        • 1.9.2.8.1 Introduction
        • 1.9.2.8.2 Technology Vendors and Service Providers
          • 1.9.2.8.2.1 Agnitio
          • 1.9.2.8.2.2 Nuance
          • 1.9.2.8.2.3 ValidSoft
          • 1.9.2.8.2.4 VoiceVault / SayPay
          • 1.9.2.8.2.5 VoiceTrust
        • 1.9.2.8.3 Advantages and Disadvantages of Voice Biometrics
      • 1.9.2.9 Finger-Vein
        • 1.9.2.9.1 Introduction
        • 1.9.2.9.2 Technology Vendors and Service Providers
          • 1.9.2.9.2.1 Hitachi
        • 1.9.2.9.3 Advantages and Disadvantages of Finger-Vein Biometrics
      • 1.9.2.10 Palm-Vein
        • 1.9.2.10.1 Introduction
        • 1.9.2.10.2 Technology Vendors and Service Providers
          • 1.9.2.10.2.1 Fujitsu
        • 1.9.2.10.3 Advantages and Disadvantages of Palm-Vein Biometrics

2 Forecasts

  • 2.1 Methodology and Assumptions
  • 2.2 Biometrics for Payments Forecasts
    • 2.2.1 Introduction
    • 2.2.2 Biometrics for Payments User Forecasts
      • 2.2.2.1 Biometrics for Payments User Forecasts - By Technology
        • 2.2.2.1.1 Biometrics for Payments User Forecasts - By Technology: Fingerprint
          • 2.2.2.1.1.1 Fingerprint Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.2 Biometrics for Payments User Forecasts - By Technology: Voice
          • 2.2.2.1.2.1 Voice Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.3 Biometrics for Payments User Forecasts - By Technology: Eye (Iris and Eye-Vein)
          • 2.2.2.1.3.1 Eye Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.4 Biometrics for Payments User Forecasts - By Technology: Behavioral
          • 2.2.2.1.4.1 Behavioral Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.5 Biometrics for Payments User Forecasts - By Technology: Finger-Vein
          • 2.2.2.1.5.1 Finger-Vein Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.6 Biometrics for Payments User Forecasts - By Technology: Palm-Vein
          • 2.2.2.1.6.1 Palm-Vein Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.6 Biometrics for Payments User Forecasts - By Technology: Heart (ECG)
          • 2.2.2.1.7.1 Heart Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.8 Biometrics for Payments User Forecasts - By Technology: Face
          • 2.2.2.1.8.1 Face Biometric Payments Users - Forecast Highlights
        • 2.2.2.1.9 Biometrics for Payments User Forecasts - By Technology: Combined
          • 2.2.2.1.9.1 Combined Biometric Payments Users - Forecast Highlights
      • 2.2.2.2 Biometrics for Payments User Forecasts - By Payment Type
        • 2.2.2.2.1 Biometrics for Payments User Forecasts - By Payment Type: Physical Retail Store - Point-of-Sale Terminal Integration
        • 2.2.2.2.2 Biometrics for Payments User Forecasts - By Payment Type: Physical Retail Store - Biometric Payment Cards
        • 2.2.2.2.3 Biometrics for Payments User Forecasts - By Payment Type: eCommerce
        • 2.2.2.2.4 Biometrics for Payments User Forecasts - By Payment Type: Mobile Payments
        • 2.2.2.2.5 Biometrics for Payments User Forecasts - By Payment Type: Wearable Payments
        • 2.2.2.2.6 Biometrics for Payments User Forecasts - By Payment Type: Bitcoin
        • 2.2.2.2.7 Biometrics for Payments User Forecasts - By Payment Type: ATM for Cash
    • 2.2.3 Biometrics for Payments Revenue Forecasts
      • 2.2.3.1 Assumption on Revenue Forecasts
      • 2.2.3.2 Biometrics for Payments Revenue Forecasts - By Technology: Fingerprint
      • 2.2.3.3 Biometrics for Payments Revenue Forecasts - By Technology: Voice
      • 2.2.3.4 Biometrics for Payments Revenue Forecasts - By Technology: Eye (Iris & Eye-Vein)
      • 2.2.3.5 Biometrics for Payments Revenue Forecasts - By Technology: Behavioral
      • 2.2.3.6 Biometrics for Payments Revenue Forecasts - By Technology: Finger-Vein
      • 2.2.3.7 Biometrics for Payments Revenue Forecasts - By Technology: Palm-Vein
      • 2.2.3.8 Biometrics for Payments Revenue Forecasts - By Technology: Heart
      • 2.2.3.9 Biometrics for Payments Revenue Forecasts - By Technology: Face
      • 2.2.3.10 Biometrics for Payments Revenue Forecasts - By Technology: Combined
    • 2.2.4 Biometrics for Payment Transaction & Transaction Value Forecasts
      • 2.2.4.1 Assumptions on Transaction & Transaction Value Forecasts
      • 2.2.4.2 Biometric for Payments Transaction Forecasts - Total Transactions
      • 2.2.4.3 Biometric for Payments Transaction Forecasts - Total Value ($)

Appendices

  • A Appendix A: Organisation Referenced in this Report
  • B How to Measure Biometric Technology? A Guide to Choosing the Right Biometric System
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