Face recognition has advanced significantly since the 1960s. State-of-the-art methods can now identify faces with over 100 times more accuracy than in the 1990s. Key datasets used for developing and evaluating face recognition algorithms include MegaFace, Labeled Faces in the Wild, and YouTube Faces DB. The general approach involves face detection, alignment, feature extraction using methods like ArcFace, and recognition. Retail is one promising use case, allowing stores to recognize customers without loyalty cards and personalize service.

1. Face recognition
and retail use case

2. About me
● 10 + working with data in different roles and domains;
● Finalist of 2 Hackathons;
● Specialization: Face recognition, customer behaviour prediction, recommendation
engine;

3. Overview
Advances in face recognition
General approach
State of the art method
Use case

4. Brief history
- Starts from 1964 at this time operators annotated 40 pictures per hour;
- 1997 ZN-Face developed through funding by US Army research laboratory used
by Deutsche bank and some airports;
- 2006 performance is evaluated in Face Recognition Grand Challenge. The results
indicated that the new algorithms are 10 times more accurate than the face
recognition algorithms of 2002 and 100 times more accurate than those of 1995

5. Available datasets
- MegaFace (4.7 m train dataset, 1m test dataset, 7 Mean photos / person (3 min,
2469 max)
- LFW Labeled Faces in the Wild (13,000 images of faces collected from the web)
- YouTube Faces DB (3,425 videos of 1,595 different people)
- IJB (A, B, C) set of up to 138000 face images, 11000 face videos, and 10000
non-face images

6. MegaFace

7. General approach
Face location
Face alignment and scaling
Feature extraction

8. Face Location
HOG
Dlib face_locations (uses cnn)
MTCNN
YoloFace
BlazeFace

9. Face landmarks

10. ArcFace for face recognition

11. Retail use case
- Substitution of loyalty cards
- Know your customer before he comes to cashier

12. Links
- http://megaface.cs.washington.edu/
- http://vis-www.cs.umass.edu/lfw/
- https://www.cs.tau.ac.il/~wolf/ytfaces/
- https://paperswithcode.com/sota/face-verification-on-megaface
- https://www.pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/