Sudha Jamthe presented Machine Learning for Cognitive IoT at IoT WIthTheBest conference online. This talk introduces Machine Leaning, and Biometrics applications. It walks us step-by-step through person identification to facial identification to facial recognition to iris recognitoon to help the IoT developer and product manager plan for biometrics capabilities to protect and authenticate Cognitive IoT Products.

1. 1
Machine Learning of Biometrics
for Cognitive IoT
SUDHA JAMTHE, CEO IoT Disruptions
IoT WithTheBest Conference, Oct 15th 2017
IoTDisruptions.com © 2017 Sudha Jamthe

2. SUDHA JAMTHE Background
2
CEO IoT Disruptions, Technology Futurist
Continuing Studies Instructor
IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe

3. What to expect in this session?
1. What is Cognitive IoT?
2. What is Machine Learning?
3. What is Biometrics?
4. Machine learning for Biometrics
5. Object recognition, Fingerprinting, Person Identification,
Facial Recognition, and Facial Identification
3

4. COGNITIVE IOT = IOT + AI
4
Machine
Learning
VR
AR
Voice to Text
Text to Speech
Facial
Recognition
Computer Vision
NLP
Machine Intelligence
IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe
Copyright: Sudha Jamthe

5. Machine Learning takes in large volume of data and makes
a decision by building Data Models (a geometric shape of
data*)
image: Techemregence Gaussian mixture model
Training
Data &
Factors
Predict
Outcomes
to make
decisions
5
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1: MACHINE LEARNING

6. Machine Learning Applications
1. Tell the self driving car to stop
2. This newsfeed is spam
3. This photo is the face of Sudha Jamthe
4. Sell this stock now
5. I recommend this movie on Netflix
6. I can confirm if this cell looks like cancer
cell or not
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7. Take a series of 2D pictures to
create a 3D analysis
e.g. Civilmaps, Robots, Wearable Pills and nanobots
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2: COMPUTER VISION

8. 8
Objects recognition – Traffic signs, cars, people
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3 : DEEP LEARNING

9. 9IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe
4: FACIAL RECOGNITION: MACHINE
LEARNING ON IMAGES

10. Types of Machine Learning Work?
1. Supervised
–Get Training Data, Define factors, Remove biases
2. Unsupervised Learning aka Deep Mining
- No need to define factors, train same architecture for
generic problem solving.
3. Reinforced Learning
- Repeated learning based on a reward.
Ref.:https://medium.com/@machadogj/ml-basics-supervised-unsupervised-and-
reinforcement-learning-b18108487c5a
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11. 11IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe
Source: blog.sas.com

12. 1
Cognitive IoT using Computer Vision
Image: Sudha Jamthe
Yumu, Collaborative Industrial Arm, ABB
Nanobots detect and kill cancer cellsKnightscope, Security Robots
IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe

13. 13
What Is Biometrics
Image: Cnet.com
Facial & Iris IdentificationFingerprinting
Identifying a person and validating it is the same person from a central
database. Used to restrict access to the right person.
IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe

14. Person Identification
1
Source Nest.com and Nest Lab
NestCam sends a notification
when it spots a person
IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe

15. 1
Biometrics: Fingerprinting
IoTDisruptions.com http://bit.ly/IoTAIBook @sujamthe

16. 16
Image: Wikipedia
Driverlesscarbook.com #Driverless @sujamthe
Biometrics: Iris Recognition
Source: indranilsinharoy.com A binary classification problem

17. Facial Recognition Research Direction
•Recognition from outdoor facial images.
•Recognition from non-frontal facial images.
•Understanding demographic factors.
•Develop improved models for predicting identification
performance on very large galleries.
•Integration of morphable models into face recognition
performance.
•Understanding the video sequences to identify faces better
• Source: http://www.face-rec.org
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18. Face recognition with Deep Learning
Source: https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-
learning-c3cffc121d78
Credit: Adam Geitgey @ageitgey
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19. Questions?
Slides are at: http://slideshare.com/sujamthe
Tweet me @sujamthe
Signup for newsletter about IoT, AI,AV:
http://iotdisruptions.com
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20. Appendix: Resources Machine Learning APIs
• Machine Learning APIs
– api.ai, Cogito, DataSift, iSpeech, Microsoft Project Oxford, Mozscape, and OpenCalais.
– Amazon, Google, IBM Watson, and Microsoft
– Amazon AWS serviceshttps://aws.amazon.com/machine-learning/
• What Machine Learning Algorithm to use when?
– https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
• Facial Recognition Apps
– https://medium.freecodecamp.org/10-best-face-recognition-apps-for-android-and-ios-652239d64203
– Face Recognition Algorithms - http://www.face-rec.org/algorithms/ (Credit: Mislav Grgic
Kresimir Delac)
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