Facial recognition is a biometric technology that uses distinct facial features to identify individuals using AI or machine learning. These sophisticated programs capture, analyze, and compare patterns in facial data. They can also detect, capture, or match a face to its owner to verify which face belongs to which person.
Baseet can auto-generate an API that you can integrate into your existing app to enable face detection and recognition – but that’s not all. We also provide a training application that you can use to generate a new model based on your own dataset.
Face recognition has countless potential enterprise applications, and Baseet helps businesses build this useful tool into their apps quickly and easily. Our user-friendly visual application builder allows you to integrate sophisticated AI capabilities into your existing code by simply copying and pasting an auto-generated API from Baseet.ai. We’ve already posted about how to accomplish this with Azure’s Face service – but if you don’t have access to this service you can still use Baseet to build or upgrade an AI app with face detection capabilities.
Enterprise Applications for Face Recognition Technology
There are many positive uses for face recognition technology that stand to benefit modern businesses. The feature is used to help you unlock your phone, go through security checkpoints, and make hands-free purchases at stores. It can also help bolster the security of financial transactions.
One of the main benefits of face recognition technology is the prevention of fraud. It can be used to identify whether a picture has been altered or photoshopped, which helps to eliminate false or misleading information on the internet. Face recognition can even help save lives. In the health sector, facial recognition has been successfully used for tracking patients’ medical information. Over the long term, this aids in the detection of genetic diseases and support for chronic health issues.
How it Works
With Baseet.ai, providing a UI and API to enable face detection and recognition in applications customized for your business is a snap. Baseet provides an auto-generated API and pre-trained model to accelerate utilities to accelerate the deployment of customized face recognition applications for your business. On our platform, you can train a face recognition model on any dataset with ease.
To add your custom-tailored face recognition solution, simply copy and paste Baseet.ai auto-generated API and add it to your existing code. From there, you can train face recognition on this custom dataset by running the Face Recognition -Train App. This will give you a model trained on the custom dataset – no data science required!
In addition to offering the opportunity to customize face recognition into your app, we also enable developers to re-train our model on their own datasets with a visual training App.
Once you’ve used the platform to develop your solution, you can use Baseet’s pre-trained “Face recognition test app,” to test your customized model. Simply drag a photo or snag one from a social network like Facebook or Twitter. Copy-and-paste our auto-generated API to embed it into your system, and you’ve got yourself a customized face detection solution. This is one of the key differentiators that sets our solution apart from the rest: Baseet.ai provides you with not only a prediction app, but also a training app
Re-train Face Detection and Recognition For Your App with Baseet
Baseet has built its own face detection module using FaceNet: A Unified Embedding for Face Recognition and Clustering. This model is trained on a subset of the MS-Celeb-1M dataset. The generated model from this app can be found in our model manager.
To access this service Baseet, you have to start with the following:
- A dataset of people images: for each person, include a folder of photos with his or her name. The custom dataset should be labeled consistent with the labels in the following image:
- Upload a dataset: go to your Baseet dashboard and click on “Datasets” from the left-hand menu. This is where you upload dataset folder (here’s a sample that we’ve named “5-celebs”):
- Next, open the Face Recognition -Train App
- Select your dataset: select the first node in the app to use the dataset you uploaded into the Dataset manager. Here’s a visual to clarify:
- Name your model: enter the name of the generated model so that it can be saved and used in the test app. Use the last node in the app to the model, and enter the name you choose using the tab at the right:
- Execute: Click the “Execute App” button, and you’ll see a successful run message. After it runs, you can see the accuracy of the model generated from the test data
The generated model will be saved in your model manager, along with the evaluation metric, which can be accessed from your Dashboard. Moreover, you can return back to the app and change the model architecture by dragging new node and change the highlighted node:
Testing Your Customized Face Detection and Recognition Model
Once you’ve trained a face detection and recognition app on your own dataset, you can now test the model with Baseet’s face detection and recognition solution customized to your data set, test the model with Baseet’s Face recognition test app. Start by dragging-and-dropping a saved photo or fetching one from a social network. If you want to use one from Twitter, you can use our Face Recognition – Twitter API to recognize faces by fetching the last image Tweeted from a certain Twitter handle.
Here’s how it works:
- First, first type the Twitter handle (below we use the example of “tw_case”) into the “twitter_handle” input field on the “Last Tweet By User” node, as well as your credential from Twitter developer and other prompted information as follows:
- From there, select the generated model from a training app, which is the third node on the top of the sequence:
- Execute the app: return back to the test app and click on Execute app, after a couple of seconds it will show the successful run in the app log
- View results: to see the result, just select the last node:
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