VisageCloud - Face Recognition meets Big Data

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VisageCloud merges state-of-the-art deep learning algorithms for face recognition and classification with data querying, tagging and querying techniques so as to empower you to leverage the full value of your data. Face recognition meets big data. In cloud or on-premise.

Technology

Agenda
 Context
 Use Cases
 Demo: what actors you look like?
 Features
 Face Detection
 Classification
 Feature Extraction & Analysis
 Competition Analysis
 Roadmap
 Technical Performance
 Offering

The Context
 Market size estimated* to reach 6.8 – 9.6 billion USD worldwide by 2022
 Machine face recognition comparable to human capabilities
 Computational effort required for analysis economically feasible
 Face analysis has attracted big players from the industry (Amazon, Microsoft)

Use Cases
 Online dating: finding members who are attractive or similar to reference photo
 In-door advertisement targeting
 In-store and omni-channel identification of returning customers
 Media and usability usability
 Sorting photo albums
 Security
 Smart surveillance
 Home surveillance
 Law enforcement
 National Biometric Identification System
 Border control

Demo: What actors you look like?
Try it on http://visagecloud.com

Features
• Find position
• Get face keypoints
• Determine
orientation
Face Detection
• Gender
• Age
• Mood
• Hair/Eye/Skin Color
Classification
• Encode face
signature
• Compare to known
persons
• Store and tag
signature
Feature Extraction
& Analysis

Face Detection
Location. Key points. Spatial orientation.

Classification
Gender. Age. Skin/eye/hair color. Mood.

Feature Extraction & Analysis
Demo: Who’s the actor?
Who’s your celebrity lookalike?

Peter Jackson Fergal Devitt Robert Swanson

Technical Performance
 Detection, analysis on a 1MP photo and comparison against 5000 reference
profiles (actors)
 Time: 2.3-2.5 seconds on one core
 Throughput/core/hour: >1000 pictures (assuming 1MP average size)
 Scalable, distributed architecture
 Executes on several multi-core machines
 Highly available data store with replication (Cassandra)

Offering
SaaS Cloud
Billed monthly
Based on
consumption
On-Premise
Up-front fee
Monthly
support fee
Consulting
Integration
Custom
Requirements
On-demand

Contact Us
office@visagecloud.com
http://visagecloud.com

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VisageCloud - Face Recognition meets Big Data

2. Agenda  Context  Use Cases  Demo: what actors you look like?  Features  Face Detection  Classification  Feature Extraction & Analysis  Competition Analysis  Roadmap  Technical Performance  Offering

3. The Context  Market size estimated* to reach 6.8 – 9.6 billion USD worldwide by 2022  Machine face recognition comparable to human capabilities  Computational effort required for analysis economically feasible  Face analysis has attracted big players from the industry (Amazon, Microsoft)

4. Use Cases  Online dating: finding members who are attractive or similar to reference photo  In-door advertisement targeting  In-store and omni-channel identification of returning customers  Media and usability usability  Sorting photo albums  Security  Smart surveillance  Home surveillance  Law enforcement  National Biometric Identification System  Border control

5. Demo: What actors you look like? Try it on http://visagecloud.com

6. Features • Find position • Get face keypoints • Determine orientation Face Detection • Gender • Age • Mood • Hair/Eye/Skin Color Classification • Encode face signature • Compare to known persons • Store and tag signature Feature Extraction & Analysis

7. Face Detection Location. Key points. Spatial orientation.

10.

11. Classification Gender. Age. Skin/eye/hair color. Mood.

12.

13. Feature Extraction & Analysis Demo: Who’s the actor? Who’s your celebrity lookalike?

14.

15.

16. Peter Jackson Fergal Devitt Robert Swanson

17. Technical Performance  Detection, analysis on a 1MP photo and comparison against 5000 reference profiles (actors)  Time: 2.3-2.5 seconds on one core  Throughput/core/hour: >1000 pictures (assuming 1MP average size)  Scalable, distributed architecture  Executes on several multi-core machines  Highly available data store with replication (Cassandra)

18. Offering SaaS Cloud Billed monthly Based on consumption On-Premise Up-front fee Monthly support fee Consulting Integration Custom Requirements On-demand

19. Contact Us office@visagecloud.com http://visagecloud.com

Editor's Notes

* http://www.marketsandmarkets.com/PressReleases/facial-recognition.asp * http://www.prnewswire.com/news-releases/facial-recognition-market-is-expected-to-reach-96-billion-worldwide-by-2022-584841741.html

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