Presentation on the basics of image identification- similarity function, Siamese network, and triplet loss

1. Face Verification and
Recognition
Nemish Kanwar
Associate Consultant- Machine Learning

2. Face
Verification
v/s Face
Recognition
Face Verification
• Checking if the
person is the one
who he claims to be
01
Face Recognition
• Who is this person?
• Outputs ID, if person
in database
02

3. Similarity function
• Degree of difference between images
𝑓 𝑥 =
same, 𝑑 𝑖𝑚𝑔1, 𝑖𝑚𝑔2 < 𝜏
different, 𝑑 𝑖𝑚𝑔1, 𝑖𝑚𝑔2 ≥ 𝜏

4. Siamese Network
• Mapping from image to Euclidian Space (encoding)
• The encoding used for face verification, recognition and Clustering
128

5. • An Image is converted to an encoding of size 128
• Images compared using encodings of two faces
SN
SN
𝑑 𝑥 𝑖 , 𝑥 𝑗 = f 𝑥 𝑖 − f 𝑥 𝑗 2
𝑦 = 1 𝑖𝑓 𝑑 < 𝜏 𝑒𝑙𝑠𝑒 0

6. Training the Network

7. Triplet Loss
Anchor Image
Positive Image
Negative Image
𝑑 𝐴, 𝑃 + 𝜶 ≤ 𝑑 𝐴, 𝑁
𝛼 = margin
𝑑 𝐴, 𝑃 − 𝑑 𝐴, 𝑁 + 𝛼 ≤ 0
𝐿 𝐴, 𝑃, 𝑁 = max(𝑑 𝐴, 𝑃 − 𝑑 𝐴, 𝑁 + 𝛼, 0)
Cost, 𝐽 =
𝑖=1
𝑚
𝐿(𝐴𝑖
, 𝑃 𝑖
, 𝑁 𝑖
)

8. Inception Block

9. Face Recognition
• Extension of Face Verification
• Each identity has there corresponding encoding pre-saved
• Incoming image is compared with each encoding
• Output: The least distance if less than threshold

10. Dog breed not found in
Database

11. Address to slides
Address to slides
https://www.slideshare.net/nemishkanwar5/face-verification-and-
recognition
Find me on Linkedin @
https://www.linkedin.com/in/nemishkanwar/