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Real time face recognition with Android + TensorFlow Lite

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Summary

This article is about creating an app that is able to accurately recognize faces on Android mobile devices in real time without an internet connection. It introduces the difference between face detection and face recognition, and discusses a mobile-oriented deep learning architecture and a demo application. The article also provides a .apk installer for the demo and links to the source code.

Q&As

Is it possible to make an app that compares faces on mobile without an Internet connection?
Yes, it is possible to make an app that compares faces on mobile without an Internet connection.

How accurate can face recognition on mobile be?
Face recognition on mobile can be very accurate.

What are the key features of this app?
The key features of this app are that it recognizes faces very accurately, works offline in real time, and uses a mobile-oriented deep learning architecture.

What is the difference between face recognition and face detection?
Face detection is the process of determining if there are people's faces present in an image and locating each face, while face recognition is the process of identifying a person from an image or video.

How does the real time face recognition app using Android + TensorFlow Lite work?
The real time face recognition app using Android + TensorFlow Lite works by using a convolutional neural network to accurately verify faces on mobile.

AI Comments

👍 This article provides a great overview of the differences between face detection and recognition, and presents a real-time working example application to accurately verify faces on mobile using state-of-the-art technology.

👎 This article is quite long and technical, and may not be suitable for readers without a background in deep learning.

AI Discussion

Me: It's about using Android and TensorFlow Lite to create real-time face recognition. It talks about how this technology can be used to accurately identify people without needing an internet connection, and how it can be done in real-time.

Friend: Wow, that's really impressive! What implications does it have?

Me: Well, it has a lot of implications. For example, it could be used to increase security in places like banks, airports, and government buildings. It could also be used for access control and identity verification. Additionally, it could be used to help identify people who may be suspected of crimes or who are missing. Finally, it could also be used for marketing purposes, such as tracking customer behavior or facial recognition-based advertising.

Action items

Research existing face recognition and detection technologies to understand the differences between them.
Develop a prototype application that uses a mobile-oriented deep learning architecture to accurately verify faces on mobile.
Test the accuracy of the application by running experiments with different types of images and data sets.

Technical terms

Real Time: Refers to a system or process that operates with very little or no delay.
TensorFlow Lite: An open source deep learning framework for mobile and embedded devices.
State-of-the-art: The highest level of development, as of a device, technique, or scientific field, achieved at any particular time.
Convolutional Neural Network: A type of artificial neural network used in image recognition and processing that is specifically designed to identify patterns in images.
Face Recognition: The process of identifying or verifying the identity of a person using their face.
Face Detection: The process of detecting the presence of faces in an image.
Face Landmarks: Specific points on a face, such as the eyes, nose, and mouth, used to identify a person.