Application of Deep Learning in Face Recognition

With the continuous development of deep learning technology, more and more application scenarios have begun to adopt face recognition technology based on deep learning. Compared with traditional feature extraction-based methods, deep learning can automatically extract high-level features in images, which has higher recognition accuracy and robustness.

This article will introduce how to use deep learning to realize face recognition, and provide Python code implementation. Specifically, we will use the Keras library in Python to build a simple convolutional neural network (CNN) for face recognition tasks.

Dataset preparation

Before performing the face recognition task, we need to prepare a data set for training and testing our model. In this article, we will use the publicly available Labeled Faces in the Wild (LFW) dataset as our dataset. This dataset contains more than 50,000 face images of more than 13,000 individuals.

First, we need to download and unzip the dataset, the code is as follows: ·

import os
import urllib.request
import zipfile

# 下载LFW数据集
url = "http://vis-www.cs.umass.edu/lfw/lfw.zip"
filename = "lfw.zip"
if not os.path.exists(filename):
    urllib.request.urlretrieve(url, filename)

# 解压数据集
with zipfile.ZipFile(filename, 'r') as zip_ref:
    zip_ref.extractall()

Next, we need to divide the images in the dataset into training and testing sets. In this article, we will use 80% of the images as the training set and 20% of the images as the test set. code show as below:

import o