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Face Recognition

In this assignment, you will build a face recognition system. Many of the ideas presented here are from FaceNet. In lecture, we also talked about DeepFace.

Face recognition problems commonly fall into two categories:

  • Face Verification - "is this the claimed person?". For example, at some airports, you can pass through customs by letting a system scan your passport and then verifying that you (the person carrying the passport) are the correct person. A mobile phone that unlocks using your face is also using face verification. This is a 1:1 matching problem.
  • Face Recognition - "who is this person?". For example, the video lecture showed a face recognition video of Baidu employees entering the office without needing to otherwise identify themselves. This is a 1:K matching problem.

FaceNet learns a neural network that encodes a face image into a vector of 128 numbers. By comparing two such vectors, you can then determine if two pictures are of the same person.

In this assignment, you will:

  • Implement the triplet loss function
  • Use a pretrained model to map face images into 128-dimensional encodings
  • Use these encodings to perform face verification and face recognition

Channels-first notation

  • In this exercise, we will be using a pre-trained model which represents ConvNet activations using a "channels first" convention, as opposed to the "channels last" convention used in lecture and previous programming assignments.
  • In other words, a batch of images will be of shape (m,nC,nH,nW)(m, n_C, n_H, n_W) instead of (m,nH,nW,nC)(m, n_H, n_W, n_C).
  • Both of these conventions have a reasonable amount of traction among open-source implementations; there isn't a uniform standard yet within the deep learning community.

Updates

If you were working on the notebook before this update...
  • The current notebook is version "3a".
  • You can find your original work saved in the notebook with the previous version name ("v3")
  • To view the file directory, go to the menu "File->Open", and this will open a new tab that shows the file directory.
List of updates
  • triplet_loss: Additional Hints added.
  • verify: Hints added.
  • who_is_it: corrected hints given in the comments.
  • Spelling and formatting updates for easier reading.
Load packages

Let's load the required packages.

from keras.models import Sequential
from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D, AveragePooling2D
from keras.layers.merge import Concatenate
from keras.layers.core import Lambda, Flatten, Dense
from keras.initializers import glorot_uniform
from keras.engine.topology import Layer
from keras import backend as K
K.set_image_data_format('channels_first')
import cv2
import os
import numpy as np
from numpy import genfromtxt
import pandas as pd
import tensorflow as tf
from fr_utils import *
from inception_blocks_v2 import *

%matplotlib inline
%load_ext autoreload
%autoreload 2

np.set_printoptions(threshold=np.nan)
Using TensorFlow backend.

0 - Naive Face Verification

In Face Verification, you're given two images and you have to determine if they are of the same person. The simplest way to do this is to compare the two images pixel-by-pixel. If the distance between the raw images are less than a chosen threshold, it may be the same person!

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**Figure 1**