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Facial Recognition with Android (2/4)

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Summary

In this article, the author covers how to add facial recognition to an Android app using Google Cloud AutoML. The author explains how to set up a service account and enable billing, how to train a model based on images and labels, how to call the Cloud AutoML service in an Android app using either a REST endpoint or the Cloud AutoML SDK, and how to link the classification results to the detected face.

Q&As

What is the purpose of this series of articles?
The purpose of this series of articles is to cover how to add facial recognition (not just facial detection) to an Android app.

What are the 4 steps for adding facial recognition to an Android app?
The 4 steps for adding facial recognition to an Android app are: Facial Detection on Android, Facial Recognition using Google AutoML (off-device), Facial Recognition using TensorFlow (off-device), and Facial Recognition using TensorFlow Lite (on-device).

What accounts need to be setup to use Google Cloud AutoML?
To use Google Cloud AutoML, a Service Account for the project and a billing account need to be setup.

What is the process for training a model on images and labels?
To train a model on images and labels, the images need to be uploaded to the AutoML website and labels need to be applied to the images. The model is then trained on the uploaded images and labels.

How can the Cloud AutoML service be called from an Android app?
The Cloud AutoML service can be called from an Android app using the REST endpoint or the Cloud AutoML SDK.

AI Comments

👍 This is a great article that explains how to use facial recognition with Android. The steps are clearly outlined and the references provide helpful additional resources.

👎 This article assumes that the reader already has a dataset ready to go. It would be helpful to provide more information on how to create and organize a dataset for training.

AI Discussion

Me: It's about using facial recognition with Android. It covers how to setup a Service Account, how to train a model with Cloud AutoML, and how to use that model in an Android app.

Friend: Interesting. What are the implications of this article?

Me: Well, it shows that it's possible to use facial recognition on Android apps. It also shows that it can be done with relative ease. However, it's important to note that the accuracy of the model will depend on the quality of the data and the amount of computing time used to train the model. Also, it's important to protect the security of the model by not including the service account key in the app.

Action items

Research how to use TensorFlow directly with Cloud AutoML.
Create a REST service to hide the private key used to access the CloudML SDK.
Experiment with larger datasets to improve the accuracy of the model.

Technical terms

Facial Recognition: A technology that uses biometric data to identify a person from a digital image or video frame.
Google Vision: A computer vision API from Google Cloud that can detect objects, faces, and landmarks in images.
Firebase ML Kit: A mobile machine learning platform from Google that provides APIs for image labeling, text recognition, and face detection.
Cloud AutoML: A Google Cloud service that enables developers to train and deploy custom machine learning models.
Service Account: A special type of Google account that is used to access Google Cloud services.
Billing: The process of paying for the use of a service or product.
Confusion Matrix: A table that is used to evaluate the performance of a classification model.
Retrofit: A type-safe HTTP client for Android and Java.
Coroutine: A lightweight thread of execution used in programming.
TensorFlow: An open source machine learning library developed by Google.
TensorFlow Lite: A lightweight version of TensorFlow designed for mobile and embedded devices.
FaceGraphic: A graphic object used to represent a face in an image.