한국해양과학기술진흥원
Mobile Grid and Cloud Computing
Opportunities and Challenges
2013.9.22
Sayed Chhattan Shah, PhD
Senior Researc...
한국해양과학기술진흥원
Outline
 Background
 Mobile Grid and Cloud Computing
 Cloud Robotics
 Mobile Ad hoc Computational Grid and...
Background
한국해양과학기술진흥원
A collection of independent computers that appear to the
users of the system as a single computer
ATM Internet...
한국해양과학기술진흥원
Types of Distributed Systems
Cluster
Grid
Cloud
한국해양과학기술진흥원
Overview: Clusters x GridsCluster - How can we use local networked resources
to achieve better performance fo...
Information
Generators
Information Distributed
Over the Grid
Customer
Access to
Information
Grid
 Computing power should ...
한국해양과학기술진흥원
Cloud Computing
Everything — from computing power to computing
infrastructure and applications are delivered a...
한국해양과학기술진흥원
Grid Computing
Computational Grids and Clusters have been extensively
deployed and widely used to solve compl...
한국해양과학기술진흥원
Grid Computing
Due to recent advances in mobile computing and
communication technologies, it has become feasi...
한국해양과학기술진흥원
Grid Computing
Several approaches have been proposed to integrate
mobile nodes with Grid and Cloud computing ...
Mobile Grid and Cloud Computing
한국해양과학기술진흥원
Mobile Cloud Computing
 Data processing and data storage happen outside of mobile devices
한국해양과학기술진흥원
Mobile Grid Computing
 Data processing and data storage happen outside of mobile devices
한국해양과학기술진흥원
Mobile Grid and Cloud Computing
 Enabling Factors
 Wireless networks
• 3G networks: 14.4 Mbps
• 4G networks:...
한국해양과학기술진흥원
Benefits
 Improved data storage capacity and processing power
 Apple’s iCloud enables users to store and syn...
Cloud Robotics
한국해양과학기술진흥원
Cloud Robotics
 Robots rely on a cloud-computing infrastructure to access
vast amounts of processing power an...
한국해양과학기술진흥원
Benefits
 Provides a shared knowledge database
 Organizes and unifies information about the world in a forma...
한국해양과학기술진흥원
Benefits
Skill / Behavior Database
 Reusable library of “skills” or behaviors that map to perceived task
req...
한국해양과학기술진흥원
Benefits
Offloads heavy computing tasks to the cloud
 Cheaper, lighter, easier-to-maintain hardware
 Longer...
한국해양과학기술진흥원
Cloud Robotics Projects
 Researchers at Social Robotics Lab have built a cloud
computing infrastructure to ge...
한국해양과학기술진흥원
Cloud Robotics Projects
 At CNRS, researcher are creating object databases for
robots to simplify the plannin...
한국해양과학기술진흥원
Cloud Robotics Projects
 Gostai, a French robotics firm, has built a cloud robotics
infrastructure called Gos...
한국해양과학기술진흥원
Cloud Robotics
 Same as:
 Remote computing?
 Mobile cloud computing?
 Mobile Grid Computing?
Computation Offloading
Migrating computation to more resourceful computers
Computation offloading = Surrogate computing = ...
한국해양과학기술진흥원
 Offloading decisions are usually made by analyzing several
parameters including
 Bandwidths
 Server speeds...
한국해양과학기술진흥원
 Offloading approaches are classified based on various factors
including
 Why to offload
• Improve performan...
한국해양과학기술진흥원
 Application partitioning
• Static vs. dynamic
 When to decide offloading
• Static vs. dynamic
 Offloading ...
한국해양과학기술진흥원
Computation Offloading
Mobile Ad hoc Computational Grid
한국해양과학기술진흥원
Mobile Ad hoc computational Grid
The mobile Grid and Cloud computing systems are
restricted to infrastructure-...
한국해양과학기술진흥원
Mobile Ad hoc computational Grid
 A distributed computing infrastructure that allows mobile
nodes to share co...
한국해양과학기술진흥원
Mobile Ad hoc computational Grid
 Computational Grid
 allows distributed computing devices to share computin...
Applications
한국해양과학기술진흥원
Autonomous Threat Detection in Urban Environments
 A group of miniature autonomous mobile robots are
deployed...
한국해양과학기술진흥원
Construction of 3D-Map and Identification of Targets within Map
A set of miniature unmanned aerial vehicles o...
한국해양과학기술진흥원
Contents
38
한국해양과학기술진흥원
Video Data Mining
Fighting units need to know activities of target in the last
60 minutes from archived video...
한국해양과학기술진흥원
Future Soldier
 In warfare soldiers may experience
physical and mental problems
 In such situations, various...
한국해양과학기술진흥원
Mobile Ad hoc Computational Grid
 Mobile ad hoc computational Grid is attractive even
when network infrastruc...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Compared to traditional parallel and distributed
computin...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
Node
RESOURCE
ALLOCATION
Node
Task
Grid
Memb...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
 Global Node Mobility
 Task Failure
 Loca...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
 To improve performance and avoid task fail...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
Node mobility
 Makes it difficult to design an efficient ...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Power management
 Main sources of energy consumption are...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Power management
 Energy efficient resource allocation s...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Constrained communication environment due limited power,
...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Dynamic network performance
 Bandwidth at different netw...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Task Migration
 To improve application performance and r...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Parallel programming model
 Programming model provides a...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Security risks
 Mobile ad hoc computational Grid may inc...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Incentive mechanism
 Assume a scenario where an individu...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Architecture for mobile ad hoc computational Grid
 Centr...
한국해양과학기술진흥원
Research Challenges and Future Research Directions
 Failure management
 Migrate the task or restart the task...
한국해양과학기술진흥원
FARE-SHARE Project
 Aims to exploit collective capabilities of nearby devices
 To execute compute-intensive ...
한국해양과학기술진흥원
 Aims to develop a system for aerial surveillance to assist a rescue team in
case of a disaster situation
 V...
한국해양과학기술진흥원
 Aims to develop a system for aerial surveillance to assist a rescue team in
case of a disaster situation
Mas...
한국해양과학기술진흥원
 Troops frequently have to wait until they’re back at camp to download latest updates
 Mission opportunities...
한국해양과학기술진흥원
Conclusion
 Due to recent advances in mobile computing and
communication technologies it has become feasible ...
Backup
한국해양과학기술진흥원
Cloud Robotics and Networked Robots
한국해양과학기술진흥원
Cloud Robotics and Networked Robots
한국해양과학기술진흥원
Cloud Robotics and Networked Robots
 Peer-based Model
 Proxy-based Model
 Clone-based Model
한국해양과학기술진흥원
Vision Understanding
 Attention Detection
 Body pose recognition
 Face detection
 Face pose recognition
 ...
Upcoming SlideShare
Loading in …5
×

Keynote on Mobile Grid and Cloud Computing

1,450 views

Published on

Background
Mobile Grid and Cloud Computing
Cloud Robotics
Mobile Ad hoc Computational Grid and Cloud
Opportunities
Research Challenges
Future Research Directions
Conclusion

Published in: Technology, Business
  • Be the first to comment

Keynote on Mobile Grid and Cloud Computing

  1. 1. 한국해양과학기술진흥원 Mobile Grid and Cloud Computing Opportunities and Challenges 2013.9.22 Sayed Chhattan Shah, PhD Senior Researcher Electronics and Telecommunications Research Institute, Korea etri.re.kr | https://sites.google.com/site/chhattanshah/
  2. 2. 한국해양과학기술진흥원 Outline  Background  Mobile Grid and Cloud Computing  Cloud Robotics  Mobile Ad hoc Computational Grid and Cloud  Opportunities  Research Challenges  Future Research Directions  Conclusion
  3. 3. Background
  4. 4. 한국해양과학기술진흥원 A collection of independent computers that appear to the users of the system as a single computer ATM Internet Distributed System
  5. 5. 한국해양과학기술진흥원 Types of Distributed Systems Cluster Grid Cloud
  6. 6. 한국해양과학기술진흥원 Overview: Clusters x GridsCluster - How can we use local networked resources to achieve better performance for large scale applications?  High-speed LAN  Centralized resource and task management How can we put together geographically distributed resources to achieve better performance?  WAN  Distributed resource and task management Cluster and Grid Computing
  7. 7. Information Generators Information Distributed Over the Grid Customer Access to Information Grid  Computing power should be available on demand, for a fee  Just like the electrical power grid Basic Idea
  8. 8. 한국해양과학기술진흥원 Cloud Computing Everything — from computing power to computing infrastructure and applications are delivered as a service
  9. 9. 한국해양과학기술진흥원 Grid Computing Computational Grids and Clusters have been extensively deployed and widely used to solve complex and challenging problems in science and engineering areas such as drug design, earthquake simulation, and climate modeling
  10. 10. 한국해양과학기술진흥원 Grid Computing Due to recent advances in mobile computing and communication technologies, it has become feasible to use mobile nodes as a contributing entity to Grids and Clouds
  11. 11. 한국해양과학기술진흥원 Grid Computing Several approaches have been proposed to integrate mobile nodes with Grid and Cloud computing systems Mobile Grid and Cloud Computing Mobile Ad hoc Grid and Cloud Computing Mobile Ad hoc Network
  12. 12. Mobile Grid and Cloud Computing
  13. 13. 한국해양과학기술진흥원 Mobile Cloud Computing  Data processing and data storage happen outside of mobile devices
  14. 14. 한국해양과학기술진흥원 Mobile Grid Computing  Data processing and data storage happen outside of mobile devices
  15. 15. 한국해양과학기술진흥원 Mobile Grid and Cloud Computing  Enabling Factors  Wireless networks • 3G networks: 14.4 Mbps • 4G networks: 100~128 Mbps
  16. 16. 한국해양과학기술진흥원 Benefits  Improved data storage capacity and processing power  Apple’s iCloud enables users to store and synchronize data in the cloud  Users can execute computationally and data-intensive applications on mobile devices  Image processing  Natural language processing  Video processing  Extended battery life  Improved reliability  Data and application are stored and backed up on a number of computers
  17. 17. Cloud Robotics
  18. 18. 한국해양과학기술진흥원 Cloud Robotics  Robots rely on a cloud-computing infrastructure to access vast amounts of processing power and data  Robots can offload heavy tasks  Image processing  Voice recognition
  19. 19. 한국해양과학기술진흥원 Benefits  Provides a shared knowledge database  Organizes and unifies information about the world in a format usable by robots  Robot Goggles  Upload images -> Download Semantic • Object name • 3D model, mass, materials, friction properties • Usage instructions - function, how to grasp, operate • Context and Domain knowledge
  20. 20. 한국해양과학기술진흥원 Benefits Skill / Behavior Database  Reusable library of “skills” or behaviors that map to perceived task requirements / complex situations  Matrix Movie Scene  For humans, still science fiction  For robots?
  21. 21. 한국해양과학기술진흥원 Benefits Offloads heavy computing tasks to the cloud  Cheaper, lighter, easier-to-maintain hardware  Longer battery life  Less need for software pushes/updates  CPU hardware upgrades are invisible & hassle-free
  22. 22. 한국해양과학기술진흥원 Cloud Robotics Projects  Researchers at Social Robotics Lab have built a cloud computing infrastructure to generate 3-D models of environments  Allowing robots to perform simultaneous localization and mapping much faster than by relying on their onboard computers • SLAM refers to a technique for a robot to build a map of the environment without a priori knowledge, and to simultaneously localize itself in the unknown environment
  23. 23. 한국해양과학기술진흥원 Cloud Robotics Projects  At CNRS, researcher are creating object databases for robots to simplify the planning of manipulation tasks like opening a door  The idea is to develop a software framework where objects come with a "user manual" for the robot to manipulate them
  24. 24. 한국해양과학기술진흥원 Cloud Robotics Projects  Gostai, a French robotics firm, has built a cloud robotics infrastructure called GostaiNet, which allows a robot to perform speech recognition, face detection, and other tasks remotely  Jazz telepresence robot uses the cloud for video recording and voice synthesis
  25. 25. 한국해양과학기술진흥원 Cloud Robotics  Same as:  Remote computing?  Mobile cloud computing?  Mobile Grid Computing?
  26. 26. Computation Offloading Migrating computation to more resourceful computers Computation offloading = Surrogate computing = Remote execution
  27. 27. 한국해양과학기술진흥원  Offloading decisions are usually made by analyzing several parameters including  Bandwidths  Server speeds  Available memory  Server loads  Amounts of data exchanged between servers and mobile systems Computation Offloading
  28. 28. 한국해양과학기술진흥원  Offloading approaches are classified based on various factors including  Why to offload • Improve performance or save energy  What mobile systems use offloading • Smart phones, robots, sensors  Infrastructures for offloading • Cluster, Grid, Cloud  Types of applications • Multimedia, gaming, calculators, text editors Computation Offloading
  29. 29. 한국해양과학기술진흥원  Application partitioning • Static vs. dynamic  When to decide offloading • Static vs. dynamic  Offloading data-intensive interdependent tasks  Offloading small tasks • May not improve performance or reduce energy consumption Computation Offloading
  30. 30. 한국해양과학기술진흥원 Computation Offloading
  31. 31. Mobile Ad hoc Computational Grid
  32. 32. 한국해양과학기술진흥원 Mobile Ad hoc computational Grid The mobile Grid and Cloud computing systems are restricted to infrastructure-based communication systems such as cellular network, and therefore cannot be used in mobile ad hoc environments
  33. 33. 한국해양과학기술진흥원 Mobile Ad hoc computational Grid  A distributed computing infrastructure that allows mobile nodes to share computing resources in mobile ad hoc environments Service Provider Node Service Provider Node Service Provider Node Service Provider Node Service Requesting Node Service Requesting Node Service Broker Node Mobile Ad hoc Network
  34. 34. 한국해양과학기술진흥원 Mobile Ad hoc computational Grid  Computational Grid  allows distributed computing devices to share computing resources to solve computationally-intensive problems  Mobile ad hoc network  a wireless network of mobile devices that communicate with each other without pre-existing network infrastructure COMPUTATIONAL GRID MOBILE AD HOC NETWORK MOBILE AD HOC COMPUTATIONAL GRID APPLICATIONS MOBILE NODES
  35. 35. Applications
  36. 36. 한국해양과학기술진흥원 Autonomous Threat Detection in Urban Environments  A group of miniature autonomous mobile robots are deployed in urban environments to detect and monitor a range of military and non-military threats  Use sophisticated image and video processing algorithms  Vision-based navigation algorithms to navigate in the environment  Beyond capabilities of single miniature mobile node
  37. 37. 한국해양과학기술진흥원 Construction of 3D-Map and Identification of Targets within Map A set of miniature unmanned aerial vehicles or mobile ro bots can be deployed in a targeted area  Broadcast live video streams  Processed to construct map and indentify stationary and mobi le targets  Requires huge processing power
  38. 38. 한국해양과학기술진흥원 Contents 38
  39. 39. 한국해양과학기술진흥원 Video Data Mining Fighting units need to know activities of target in the last 60 minutes from archived video content which requires storing live video content  To store content, a large amount of storage space is required  Processing of stored video content according to user demand also requires large amounts of processing power  Nodes owned by soldiers or fighting units can form an ad hoc data and computational Grid
  40. 40. 한국해양과학기술진흥원 Future Soldier  In warfare soldiers may experience physical and mental problems  In such situations, various biomedical devices can be used to continuously monitor the soldiers' psychophysiological health  Data from devices can be used to assess physical and mental health  Soldiers also need to rely on various sensing, processing and communication systems in the vicinity to achieve situational awareness and understanding of the battlefield  Simultaneously executing computationally-intensive models for deriving physiological parameters and for acquiring battlefield awareness in real time requires computing capabilities that go beyond those of an individual sensing and processing devices
  41. 41. 한국해양과학기술진흥원 Mobile Ad hoc Computational Grid  Mobile ad hoc computational Grid is attractive even when network infrastructure is available  Short-range wireless communication consumes less energy and provides faster connectivity  3G networks: 2~14.4 Mbps  4G networks: 100~128 Mbps  Wi-Fi LAN 400Mbps
  42. 42. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Compared to traditional parallel and distributed computing systems such as Grid and Cloud mobile ad hoc computational Grid is characterized by  Node mobility  Limited battery power  Low bandwidth and high latency  Shared and unreliable communication medium  Infrastructure-less network environment • No one is in charge • No one to provide standard service
  43. 43. 한국해양과학기술진흥원 Research Challenges and Future Research Directions Node mobility Node RESOURCE ALLOCATION Node Task Grid Members Task Queue Task NODE SELECTION DISPATCHER
  44. 44. 한국해양과학기술진흥원 Research Challenges and Future Research Directions Node mobility  Global Node Mobility  Task Failure  Local Node Mobility  Increased data transfer times  Mobility of an Intermediate Node  Increased data transfer times and may disconnect network  Approaches:  Task migration  Task reallocation  In both cases, delay due to reallocation or migration of task
  45. 45. 한국해양과학기술진흥원 Research Challenges and Future Research Directions Node mobility  To improve performance and avoid task failure or migration, nodes with long-term connectivity are required for the allocation of tasks  An effective and robust two-phase resource allocation scheme  Exploit the history of user’s mobility patterns in order to select nodes that provide long-term connectivity  Location prediction schemes  Use node’s direction and speed to predict future connectivity
  46. 46. 한국해양과학기술진흥원 Research Challenges and Future Research Directions Node mobility  Makes it difficult to design an efficient and robust resource discovery and monitoring system  After reporting status a node may move across the coverage area  Grid management system would assume that status is valid and would make decisions accordingly  To avoid this problem • Proactive approach  Resources can be monitored continuously or with minimum update interval  In both cases, there will be a communication overhead • Use reactive approach  Reduces communication cost but introduces delay
  47. 47. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Power management  Main sources of energy consumption are CPU processing, memory, and data transmission in the network  Key factors that contribute to transmission energy consumption • transmission power required to transmit data and • communication cost induced by data transfers between tasks  Most of the schemes are focused on the conservation of processing energy  Saving energy in data transfers between tasks remains an open problem • becomes even more critical for data-intensive parallel applications
  48. 48. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Power management  Energy efficient resource allocation scheme • Aims to reduce transmission energy consumption and data transfer cost • Basic idea is to allocate tasks to nodes that are accessible at minimum transmission power Y 1TPL X 1TPL 3TPL 4TPL2TPL
  49. 49. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Constrained communication environment due limited power, shared medium and node mobility  Suffers from low bandwidth, high latency and unstable connectivity problems  In such an environment, data transfer cost is very critical for application and system performance  To reduce data transfer costs, directional antennas, efficient medium access control, channel switching, and multiple radios are a few promising approaches  Parallel applications usually consist of a range of tasks with varying bandwidth, processing, and deadline constraints  Work is needed to develop a Grid management system that should exploit a diverse range of links, node capabilities, and application’s characteristics
  50. 50. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Dynamic network performance  Bandwidth at different network portions varies over the time and different nodes often experience different connection quality at the same time due to the traffic load and communication constraints  Grid management system that should consider network dynamics particularly when data-intensive interdependent tasks need to be allocated
  51. 51. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Task Migration  To improve application performance and resource utilization, and to avoid task failure and load imbalance  Most common migration strategy is to estimate migration cost and determine task completion time before and after the migration of task  However, estimation of migration cost particularly of data intensive task is not straightforward due to dynamic communication environment  How to estimate data transfer time?  In addition, this strategy works well when amount of data transmitted or processed by a task is known in advance
  52. 52. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Parallel programming model  Programming model provides an abstract view of computing system  The traditional parallel programming models do not deal well with communication issues • Therefore are not suitable for mobile ad hoc environments where communication latencies and link failure and activation ratios are too high  Actor-based programming model could be the possible candidate because it deals quite well with high latencies, offers lightweight migration and can be easily adopted to deal with node mobility
  53. 53. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Security risks  Mobile ad hoc computational Grid may include heterogeneous devices owned by various individuals, organizations and groups  can be used in various scenarios such as military, disaster relief and urban surveillance where security is a primary concern  Compared to traditional wired and wireless networks, design of an efficient security system for mobile ad hoc computational Grid is a challenging task • due infrastructure-less network environment, shared communication medium, and node mobility
  54. 54. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Incentive mechanism  Assume a scenario where an individual travelling with strangers requires additional computing resources to perform a computationally intensive task • The problem is how to or what will motivate an individual to share her resources with a stranger?  To address this problem, a few solutions have been proposed in the literature where either battery power or processing cycles are traded • Effective when both parties are in need of resources from each other  The design of an incentive mechanism for mobile ad hoc computational Grids is difficult due to lack of central authority and ad hoc system architecture
  55. 55. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Architecture for mobile ad hoc computational Grid  Centralized • Single point of failure and scalability  Decentralised • Group management • Ineffective resource allocation  Distributed • Ineffective resource allocation  Hybrid architecture
  56. 56. 한국해양과학기술진흥원 Research Challenges and Future Research Directions  Failure management  Migrate the task or restart the task on another node  estimation of task completion time with and without migration cost?  Quality of Service support  application’s demands such as energy, bandwidth guarantees and real-time services  Standards for heterogeneous environments  Wireless Communication Technologies
  57. 57. 한국해양과학기술진흥원 FARE-SHARE Project  Aims to exploit collective capabilities of nearby devices  To execute compute-intensive models for deriving physiological parameters and for acquiring context awareness in real time
  58. 58. 한국해양과학기술진흥원  Aims to develop a system for aerial surveillance to assist a rescue team in case of a disaster situation  Video data is submitted to an evaluation system via a high performance communication network where a 3D virtual world is created in quasi real time Collaborative Drones
  59. 59. 한국해양과학기술진흥원  Aims to develop a system for aerial surveillance to assist a rescue team in case of a disaster situation Master-Slave Collaborative UAV Surveillance System Architecture
  60. 60. 한국해양과학기술진흥원  Troops frequently have to wait until they’re back at camp to download latest updates  Mission opportunities may erode because the information needed at the tactical edge isn’t immediately available  CBMEN program aims to rapidly share up-to-date imagery, maps and other vital information directly among front-line units  Each squad member’s mobile device function as a server, so content is generated, distributed and maintained at the tactical edge where it’s needed  A key factor that enables CBMEN is the tremendous computing power available in current mobile devices  64 gigabytes of storage in a single smartphone  A squad of nine troops could have more than half a terabyte (500 GB) of cloud storage Content-Based Mobile Edge Networking Program
  61. 61. 한국해양과학기술진흥원 Conclusion  Due to recent advances in mobile computing and communication technologies it has become feasible to design and develop next generation of distributed applications through sharing of computing resources in mobile and ad hoc environments  Further investigation is required  Resource Management  Programming model  Communication performance  Mobility  QoS support
  62. 62. Backup
  63. 63. 한국해양과학기술진흥원 Cloud Robotics and Networked Robots
  64. 64. 한국해양과학기술진흥원 Cloud Robotics and Networked Robots
  65. 65. 한국해양과학기술진흥원 Cloud Robotics and Networked Robots  Peer-based Model  Proxy-based Model  Clone-based Model
  66. 66. 한국해양과학기술진흥원 Vision Understanding  Attention Detection  Body pose recognition  Face detection  Face pose recognition  Eye detection  Lip Motion Detection  Face & eye tracking  Mouth location & tracking  Speaking recognition (spatial-temporal analysis)  Facial Expression and Emotion  Local feature analysis  Global face pattern analysis  Online face learning and recognition

×