Developing face recognition - Cloud Assisted Services

Developing face recognition technology
for the KC Class Biometrics service
Vitomir Štruc, Jerneja Žganec Gros

Outline
• Introduction
• Alpineon
• Face recognition in the cloud
– Overview and characteristics
– Challenges and obstacles
– Standards and recommendations
– Existing solutions
• Our research work
• Conclusion
CLASS Conference 2012, Bled, Slovenia, October 22-25

Introduction
• Face recognition technology slowly matures (research
focused on real-life applications) – engines evolve
• Future challenges
– Processing power
– Scalability issuess
– Accessibility issues
• Viable solution
– Cloud computing (emense potential market value)
CLASS Conference 2012, Bled, Slovenia, October 22-25

Alpineon
• SME with R&D experience in the field of ICT technologies
• Software development
• CTI (Computer telephony integration) applications
• Slovenian text-to-speech synthesis (TTS)
• Automatic speech recognition (ASR) applications
• Speech-to-speech translation (STS)
• Biometric technology
• Computer vision applications
• Hardware development
CLASS Conference 2012, Bled, Slovenia, October 22-25

Alpineon
• Past R&D projects (some examples):
– NATO IST-078 Machine Translation for Coalition Operations,2008-2012
• Goal: developing statistical machine translation
– Eureka projekt E!3864 TESTED, 2009-2012
• Goal: developing TTS for embedded devices
– VoiceTRAN II, 2006-2008
• Goal: developing the VoiceTRAN Communicator – a multimodal translation aid that
translates phrases from the source language into the target language
– AvID 2007-2009
• Goal: developing a multimodal (audio-video) system for detecting and recognizing
the psycho-physical state of a speaker
CLASS Conference 2012, Bled, Slovenia, October 22-25

Alpineon
• What we are doing in the scope of KC CLASS.
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition in the cloud
• Overview
– FR systems comprise components for (regardless of the
platform):
• data acquisition (sensor)
• template generation (engine)
• matching (engine)
• Template storage (database)
– FR in the cloud:
• Engine located in the cloud
• Sensor located on client side
• Database stored locally or in the cloud
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition in the cloud
• Our design choices
– Engine implemented for the cloud (REST API)
– Database hosted locally
• Consequences:
– Easy integration
– Accesability
– Scalability (in terms of
number of simultaneous accesses)
– „Limited storage“
– Compliant with legislation
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition in the cloud
• Engine demo (desktop version – identification mode):
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition in the cloud
• Challenges and obstacles
– Meeting peformance criteria with respect to recognition engine
• Systematic scientific methodology (databases and protocols)
– Selecting the most appropriate platform
• Depending on needs, characteristics of the final product, requirements
– Legislation pertaining to data protection, data retention, privacy
• Different from country to country
• In Slovenia: ZVOP-1 applies to template storage
• Information commisioner provides guidelines for biometric and cloud
technologies
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition in the cloud
• Standards and recommendations
– Several standards and recommendations exist for FR and CC
• Internet protocols, data formats, comunication protocols, …
– Most important:
• Biometric data interchange formats – Part 5: Face image analysis (ISO)
• Face recognition format for data interchange (ANSI INCIST)
– Allow for interoperability among vendors
– Define full-frontal, token images (rely on eye centers) and quality standards
• OASIS: Biometric Identity Assurance Services (BIAS)
– SOAP specifications for biometric services (with reference implementation)
– Internationalization on the way (ISO/IEC JTC 001/SC 37)
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition in the cloud
• Deployment possibilities and existing solutions
– Deployment options:
• Smart spaces, ambient intelligence environments, access control
applications, mobile applications, social networks, …
– Existing solutions:
• Animetrics
• BioID
• Face.COM (recently bought by Facebook) – rummored 180M $
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition and KC CLASS
• Our research work and scientific contributions:
– B. Vesnicer, J. Žganec Gros, N. Pavešić and V. Štruc, Face Recognition using
Simplified Probabilistic Linear Discriminant Analysis, International Journal of
Advanced Robotic Systems (SCI), paper in press. 2012.
– Vitomir Štruc, J. Žganec Gros, and N. Pavešić, Non-parametric score
normalization for biometric verification systems, Proceedings of the
International Conference on Pattern Recognition (ICPR 2012), Japan,
November 2012.
– Vitomir Štruc, J. Žganec Gros, and N. Pavešić. Advanced correlation filters for
facial landmark localization. In: ZAJC, Baldomir (Ed.), TROST, Andrej (Ed.). ERK
2012, September 2012, Portorož, Slovenia, vol. B, pp. 153-156, 2012.
CLASS Conference 2012, Bled, Slovenia, October 22-25

Face recognition and KC CLASS
• Our research work and scientific contributions:
– Demo on correlation filters
CLASS Conference 2012, Bled, Slovenia, October 22-25

Conclusion
• Cloud-based face recognition technology has an emense
potential market value
• There are still open issues that need to be resolved
• Thanks to our sponsors!
CLASS Conference 2012, Bled, Slovenia, October 22-25

Thank you for your attention!
Questions?
For more information, please contact:
vitomir.struc@alpineon.com, jerneja.gros@alpineon.com
CLASS Conference 2012, Bled, Slovenia, October 22-25
www.kc-class.eu