## face recognition

ke, 21 July 2018

[deep_learning

]
- face recognition

face verification:

Input image, name/ID

Output whether the input image is that of the claimed person

1:1

face recognition:

Has a database of K persons

Get an input image

Output ID if the image is any of the K persons ( or “not recognized”)

1:K

- One-shot learning

Learning a “similarity” function

d(img1, img2) = degree of difference between images

if d(img1, img2) <= t “same”

else >t “different”

- Siamese network

Goal of learning

Parameters of NN define an encoding f(x(i))

Learn parameters so that:

If x(i),x(j) are the same person, ||f(x(i)-f(x(j)))||^2 is small

[Taigman et. al., 2014. DeepFace closing the gap to human level performance]

- Triplet loss

learning_objective

want: ||f(A) - f(P)||^2 <= ||f(A) - f(N)||^2

||f(A) - f(P)||^2 - ||f(A) - f(N)||^2 <= 0

||f(A) - f(P)||^2 - ||f(A) - f(N)||^2 + alpha <= 0

Loss function:

L(A,P,N) = max(||f(A) - f(P)||^2 - ||f(A) - f(N)||^2 + alpha, 0)

J = E(i=1,m) L(A(i), P(i), N(i))

choose triplets that’re “hard” to train on.

- Face verification and binary classification

[Taigman et. al., 2014. DeepFace closing the gap to human level performance]