The ability to detect, track, and analyze faces opens up a wide range of interesting use cases, ranging from interactive smart applications and real-time video processing, all the way to biometric security and augmented reality.
This talk will showcase the available tools built by the Python community and their corresponding pros & cons, limitations, and complexity. While discussing the possible scenarios and what is actually required to DIY with Python, I will compare such handmade solutions with Cloud-based products and APIs.
16. Face DetecCon Techniques -‐ HOG
clda.co/pycon8-‐facial-‐analysis
Histogram of Oriented Gradients
17. 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
18. Face DetecCon Techniques -‐ Haar Cascades
clda.co/pycon8-‐facial-‐analysis
Haar Feature-‐based Cascade Classifiers