This document discusses the challenges of face recognition technology. It addresses deep learning challenges, business challenges, and ethical management challenges. It discusses techniques like Angular Margin Loss to enhance discriminative power in face recognition algorithms. It also addresses issues like algorithmic bias, data bias, and lawsuits related to large face datasets. The document emphasizes the need to build face recognition products in an ethical manner while also dealing with unrealistic requests and attempts to fool the technology.

1. FACE
RECOGNITION
TECHNOLGY
HOW TO BEAT THE CHALLENGES?
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DEEP TECH CONFERENCE | CODIAX - NOV 2019 | CINDY SPELT

2. CODIAX - NOVEMBER 2019
"TAKE A SEAT AT THE
TABLE WHEN A TOPIC
REALLY MATTERS."
CINDY SPELT | FOUNDER ZOOM IN & ZOOM OUT
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3. CHALLENGES
DEEP LEARNING
BUSINESS
ETHICAL MANAGEMENT
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4. DEEP LEARNING
ARCFACE: Angular Margin Loss
Appropriate loss functions to
enhance discriminative power in
order to achieve intra-class
compactness.
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5. HOW TO BEAT
THE STATUS QUO?
From algorithmic bias to data bias.
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6. MegaFace: 4 million photos
= $ 35 billion lawsuit06

7. NERVES OF STEEL
How to survive in a rapidly changing
environment and deal with unrealistic
market requests?
How to build the perfect product by
taking advantage of people trying to fool
the tech?
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8. ETHICAL MANAGEMENT
How do we - as humans - want to engage with each other?
How to step away from fear and thrive? 08

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