facial analysis techniques for pythonista (and beyond!) - pycon8
TRANSCRIPT
clda.co/pycon8-‐facial-‐analysis
Facial Analysis Techniques for Pythonista
(and beyond!)
4/9/2017 PYCON OTTO @ Florence
About Me
@alex_casalboni
clda.co/pycon8-‐facial-‐analysis
Computer Science Background
Master in Sound & Music Engineering
Sr. SoDware Engineer & Web Developer
Cloud Evangelist @ Cloud Academy
Agenda
What is a Face?
Problem decomposiLon
AlternaLves to DIY
clda.co/pycon8-‐facial-‐analysis
What is a Face?
You thought you knew, but you didn’t… ?
clda.co/pycon8-‐facial-‐analysis
About “Normal” Faces
clda.co/pycon8-‐facial-‐analysis
What about rotaCon invariance?
clda.co/pycon8-‐facial-‐analysis
What about animals?
clda.co/pycon8-‐facial-‐analysis
What about painCngs?
clda.co/pycon8-‐facial-‐analysis
What about masks?
clda.co/pycon8-‐facial-‐analysis
What about smilies?
clda.co/pycon8-‐facial-‐analysis
Problem decomposiLon
What are the main building blocks for facial analysis?
clda.co/pycon8-‐facial-‐analysis
Face DetecCon
clda.co/pycon8-‐facial-‐analysis
Input: 1 image & unknown context
Goal: finding faces (how many?)
Output: lists of coordinates
Difficulty: preUy easy
Face DetecCon Results
clda.co/pycon8-‐facial-‐analysis
Face DetecCon Techniques
clda.co/pycon8-‐facial-‐analysis
Algorithmical techniques
Not too much “intelligence”
Real-‐Lme tracking
Face DetecCon Techniques -‐ HOG
clda.co/pycon8-‐facial-‐analysis
Histogram of Oriented Gradients
HOG w/ OpenCV and dlib
clda.co/pycon8-‐facial-‐analysis
* Vectors allow for more advanced analysis (see hUp://www.paulvangent.com/2016/08/05/emoLon-‐recogniLon-‐using-‐facial-‐landmarks/)* That .dat file is 100+MB
Face DetecCon Techniques -‐ Haar Cascades
clda.co/pycon8-‐facial-‐analysis
Haar Feature-‐based Cascade Classifiers
Haar Cascades w/ OpenCV
clda.co/pycon8-‐facial-‐analysis
Face RecogniCon
clda.co/pycon8-‐facial-‐analysis
Input: 1 reference and 1 target image
Goal: finding facial matches
Output: lists of (potenLal) matches
Difficulty: medium
Facial Encoding
clda.co/pycon8-‐facial-‐analysis
Vector RepresentaLon (128D) *
* could be learned with DL
Facial Distance
clda.co/pycon8-‐facial-‐analysis
A and B are the same person if distance(A, B) < tolerance
Face Matching w/ face_recogni2on
clda.co/pycon8-‐facial-‐analysis
Facial Analysis
clda.co/pycon8-‐facial-‐analysis
Input: 1 detected face
Goal: extracLng high-‐level informaLon
Output: gender, age, emoLons, headwear, etc.
Difficulty: preUy hard
Facial Analysis
clda.co/pycon8-‐facial-‐analysis
ML Model (gender)
ML Model (emoLons)
ML Model (….)
ML Model (age)
ML Model (headwear)
Facial Analysis
clda.co/pycon8-‐facial-‐analysis
How many training sets?
Parallel features extracLon & predicLon
Accuracy is more subjecLve (source/target audience)
Real-‐Lme is not guaranteed
AlternaLves to DIY
How about Facial Analysis services?
clda.co/pycon8-‐facial-‐analysis
Facial Analysis Services
Amazon RekogniLon
Google Cloud Vision
Azure Face API
Face++
Kairos
EmoVu
clda.co/pycon8-‐facial-‐analysis
Amazon RekogniCon & Python
clda.co/pycon8-‐facial-‐analysis
Google Cloud Vision & Python
clda.co/pycon8-‐facial-‐analysis
Azure Face API & Python
clda.co/pycon8-‐facial-‐analysis
clda.co/pycon8-‐facial-‐analysis
Cloud Services Pros
Language agnosLc (RESTful APIs)
Models are updated under the hood
No infrastructure to manage
PAYG model (w/ free Ler)
Great for embedded systems
Granted accuracy (globally)
clda.co/pycon8-‐facial-‐analysis
Cloud Services Cons
Hardly real-‐Lme (HTTPs calls)
ConnecLvity is always needed
Training set is never customizable
ML Models are a black box
Thank you :)
jobs.cloudacademy.com
P.S. we are hiring!
PYCON OTTO @ Florence