Face Recognition System

by   Yang Li, et al.

Deep learning is one of the new and important branches in machine learning. Deep learning refers to a set of algorithms that solve various problems such as images and texts by using various machine learning algorithms in multi-layer neural networks. Deep learning can be classified as a neural network from the general category, but there are many changes in the concrete realization. At the core of deep learning is feature learning, which is designed to obtain hierarchical information through hierarchical networks, so as to solve the important problems that previously required artificial design features. Deep Learning is a framework that contains several important algorithms. For different applications (images, voice, text), you need to use different network models to achieve better results. With the development of deep learning and the introduction of deep convolutional neural networks, the accuracy and speed of face recognition have made great strides. However, as we said above, the results from different networks and models are very different. In this paper, facial features are extracted by merging and comparing multiple models, and then a deep neural network is constructed to train and construct the combined features. In this way, the advantages of multiple models can be combined to mention the recognition accuracy. After getting a model with high accuracy, we build a product model. This article compares the pure-client model with the server-client model, analyzes the pros and cons of the two models, and analyzes the various commercial products that are required for the server-client model.


page 8

page 23

page 24

page 27

page 28

page 31

page 32


Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN

The way to accurately and effectively identify people has always been an...

DeepID3: Face Recognition with Very Deep Neural Networks

The state-of-the-art of face recognition has been significantly advanced...

An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)

The exponential growth in the number of complex datasets every year requ...

Implementation of Training Convolutional Neural Networks

Deep learning refers to the shining branch of machine learning that is b...

Distinction between features extracted using deep belief networks

Data representation is an important pre-processing step in many machine ...

Multi-limb Split Learning for Tumor Classification on Vertically Distributed Data

Brain tumors are one of the life-threatening forms of cancer. Previous s...

Deep fusion of visual signatures for client-server facial analysis

Facial analysis is a key technology for enabling human-machine interacti...

Please sign up or login with your details

Forgot password? Click here to reset