How image recognition apps have become so popular today

Image recognition technology is becoming more and more active in our daily life. Companies and inventors apply it to everything from security to customer satisfaction services. That’s why, nowadays, you can also find qualified specialists who tell you how to make an image recognition app in detail. So, let’s find out lots of interesting facts about image recognition technologies!

Where can you implement image recognition?

Investments in projects based on image recognition technology are permanently growing. Here are just a few examples of how they are used in multiple niches:

  1. Healthcare. Illness does not paint anyone. But there are diseases in which changes in appearance are especially noticeable. By comparing the patient’s appearance with images from the database, doctors can more accurately diagnose and even determine the severity of the patient’s pain.
  2. Tourism. Have you seen an interesting photo on Instagram and want to dine in the same cafe? How do you like the idea of ​​uploading a photo to an application that will take you to the right place? For instance, based on Instagram photos, an image recognition app by Perpetio creates ratings of the best places for tourists.
  3. Transport. The ability to recognize images is a function without which a drone cannot travel a couple of meters without an accident. But if you have ordinary cars in your fleet, facial recognition is also useful – for example, to prevent unauthorized use of vehicles or inattentive behavior of the driver behind the wheel.
  4. Commerce. Here, the pattern recognition function is used most actively. eBay and Boohoo online stores help users find the right product even if the buyer can’t remember the name of the item.

Do you think all this is only for very large and rich companies? Indeed, implementing machine learning (on which pattern recognition is based) from scratch can be a hassle. Fortunately, there are libraries that provide the ability to use ready-made models when developing your products. One of them is Firebase ML Kit. And then we will tell you how to use it when creating your application.

How image recognition works

Pattern recognition can be performed using either alternative image processing techniques or modern deep learning networks:

  1. Image processing methods generally do not require specific training data and are inherently unsupervised. OpenCV is a popular tool for image processing tasks.
  2. Deep learning methods are generally tied to supervised or unsupervised learning. Performance is limited by the processing power of GPUs, which is growing exponentially every year.

Face and people recognition

Most face recognition systems are based on object recognition. Face detection is one of the most famous tools for object detection, and you possibly already use it whenever you unlock your phone with your face.

Intelligent video analysis

Object detection is important for intelligent video analytics (IVA) no matter if cameras are available in retail locations to see how shoppers cooperate with products. These video streams go through an anonymization pipeline to blur people’s faces and depersonalize them.

Intelligent video surgery

Surgical video is very necessary data that is formed by endoscopes during critical actions. Object detection can be used to locate objects that are difficult to see, such as polyps or lesions, that require immediate intervention by the surgeon. It is also used to inform the hospital staff about the status of the operation.

Pedestrian detection

This is one of the most important tasks of computer vision, which is used in robotics, video surveillance, and automotive security.

Yet, despite its comparatively high performance, this approach still faces risks such as different varied types of clothing looks or the presence of covering accessories that reduce the accuracy of current sensors.

What is Firebase ML Kit and how it works

ML Kit is an SDK that provides the ability to use Google’s ready-made machine-learning solutions for iOS and Android in a simple way. Whether or not you have experience using machine learning, you can implement the functionality you need in a few lines of code. If you are an experienced machine learning developer, you can upload your own models.

ML Kit can work both online (and you get free access to a much more solid database, but with a request limit: only the first thousand are free) and offline. Features such as textual content, codes, and picture acknowledgment are accessible both online and offline. Landmark recognition (famous houses, rivers, streets, etc.) is only available online, while face recognition is only available offline on the device.

Examples of machine learning

Some examples of using machine learning:

  1. Data classification: Machine learning models can be used to divide data into specific categories by recognizing patterns and relationships in the data. For example, this can be used to predict outcomes such as predicting customer behavior or predicting weather conditions.
  2. Regression: Machine learning models can be used to explore the relationship between different variables and make predictions for the future. This can be used, for example, to predict house or stock prices.
  3. Clustering: Machine learning models can be used to divide data into groups that have similar characteristics. This can be used, for example, to analyze the behavior of customers or social networks.

Summing it up

The use of machine learning, in particular, the function of pattern recognition, does not necessarily imply anything out of the ordinary. For instance, in your app, this might just be checking for a user-added photo during the registration process. You need to make sure that the user has not added, for example, a photo of his cat, and the client wants to quickly start using the service and not wait for the moderator’s approval.

It is not necessary to invest large funds to implement the image recognition function in the application. You can fully or partially use ready-made solutions, one of which is the Firebase ML Kit. We hope that the algorithm of actions outlined by us will allow us to do this without making an excessive effort. If you want to be completely sure of the result, please contact the Perpetio company. They are always ready to help you develop web services and mobile applications.