Demand for Driverless Cars and Smart Driver Assistance Systems Expands Facial Recognition Applications
The public transportation and infrastructure sectors in the transport industry are characterized with a constant crowd flow and a large number of foreign visitors. In order to smooth out the flow of crowds and protect national security and tourists, biometric recognition technology that can shorten immigration inspection time and track suspects is increasingly needed, especially after the 911 terrorism attack. The biometric recognition technology currently employed by the transport industry for identification relies on biometric traits such as fingerprint, iris, and facial data. This report looks into a variety of application cases to analyze the use of facial recognition technology in the transport industry and the potential of the technology.
In line with the megatrend towards smarter and informative vehicles, automakers have aggressively developed new technologies and solutions to address three major issues: the connection between the driver and the road conditions like driving safety and traffic jams, the vehicle conditions such as the theft protection feature, and the connection between the driver and the vehicle. "Facial recognition technology can solve all these issues," says Chung-yu Yang, industry analyst.
List of Topics:
- Development of facial recognition technology in the transport industry, touching on three purchases of the technology which are ID verification, attribute analysis, and human-machine interface
- Application cases associating with transport management systems, which are mainly used for immigration inspection and crime tracking, and includes the detailed view of two cases: automated gate at Japanese Narita Airport, and security system at China's Hohhot Railway Bureau.
- Application cases in the air transport industry and land transport industry, where facial recognition technology is mainly used for crime prevention and permission management, and includes the detailed view of three cases: KLM's boarding system, Uber's driver verification system in the United States, and Chinese Didi Chuxing's car rental system.
- Application cases in the car and automotive electronics industries where the facial recognition technology is mainly used for driver alerts and behavior analysis, and includes the detailed view of two cases: Japanese Omron's driver concentration sensing technology and German Bosch's concept car.
- Analysis of the application cases of facial recognition, touching on how the technology has been employed in public transport, online car hailing services, and the car industry
- Outlook for financial recognition in the transport industry, touching on opportunities in the areas of public transport and smart car.
Table of Contents
1. Introduction to Facial Recognition Technology
Samples
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Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Advicy Drive
- Affectiva
- Alibaba
- Apple
- Beijing Xiaoju Technology
- BMW
- Bosch
- British Airway
- Careem
- Craft Silicon
- Denso
- Didi Chuxing
- Empatica
- Fiat Chrysler Automobiles
- Ford
- Gentex
- Gestigon
- Grab
- Honda
- Jaguar Land Rover
- KLM Royal Dutch Airlines
- Lexus
- Media Lab
- Omron
- Outerspace Design
- Panasonic
- Sober Steering
- Softbank
- Synaptics
- Tencent
- Thanko
- Uber
- Vigo
- VoicalZoo
Methodology
Primary research with a holistic, cross-domain approach
The exhaustive primary research methods are central to the value that the analyst delivers. A combination of questionnaires and on-site visits to the major manufacturers provides a first view of the latest data and trends. Information is subsequently validated by interviews with the manufacturers' suppliers and customers, covering a holistic industry value chain. This process is backed up by a cross-domain team-based approach, creating an interlaced network across numerous interrelated components and system-level devices to ensure statistical integrity and provide in-depth insight.
Complementing primary research is a running database and secondary research of industry and market information. Dedicated research into the macro-environmental trends shaping the ICT industry also allows the analyst to forecast future development trends and generate foresight perspectives. With more than 20 years of experience and endeavors in research, the methods and methodologies include:
Method
- Component supplier interviews
- System supplier interviews
- User interviews
- Channel interviews
- IPO interviews
- Focus groups
- Consumer surveys
- Production databases
- Financial data
- Custom databases
Methodology
- Technology forecasting and assessment
- Product assessment and selection
- Product life cycles
- Added value analysis
- Market trends
- Scenario analysis
- Competitor analysis
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