Everyone has tried Face ID (Facial Recognition) to unlock their phone, image search on Google or Pinterest, or browse Amazon recommendations based on searched-for products.
Have you ever wondered how all of this is possible? All above-mentioned features that make life so much more convenient involve AI (Artificial Intelligence) in general and AI image recognition in particular. Let’s learn more about what AI is, how it works, and what benefits it provides business owners.
AI Image recognition, also known as “image classification,” is a subcategory of computer vision. It is a set of methods for detecting and analyzing images that is capable of identifying people, objects, places, and other elements within an image. It is the final stage of image processing, which in turn is one of the most important tasks of computer vision.
AI image recognition uses the following techniques.
● Classification. The process of identification of the category where an image belongs. An image can be assigned to only one category.
● Tagging. A classification task with a higher degree of accuracy that can recognize different objects in one image. Thus, there can be more than one tag assigned to one image.
● Detection. Placing a bounding box around the object of interest in an image. This step is necessary to locate an object.
● Segmentation. A detection task that is necessary when being extremely accurate and precise, important because it can locate an element of an image to the nearest pixel.
The basic principle of AI image recognition
AI image recognition is based on the technology of deep learning and the concept of neural networks.
Deep learning is a subset of machine learning. It is a neural network with three or more layers that attempts to simulate the ability of the human brain to process constantly, allowing the neural network to “learn” from large amounts of data. The more layers a neural network has, the more optimized and accurate the predictions it makes.
Deep learning technology is widely used in everyday services that improve automation, performing analytics and physical tasks without human intervention -- for example, self-driving cars, digital assistants, and voice-enabled TV remotes.
● Supervised learning
Supervised learning is also known as a classification algorithm, wherein learning is based on a labeled or tagged training dataset. For example, if one wants the AI image recognition system to identify cats, one inputs the dataset which consists of images of cats and some other class of animals, for example, dogs. Then one tells the computer, by labeling each image, whether it contains a cat, a dog, or neither. Based on the input data, the system will build statistically meaningful relationships between the features to identify patterns.
Since the supervised learning method requires a labeled training dataset, significant upfront human intervention for labeling data appropriately is needed. That in turn leads to an increase in cost, compared to unlabeled, unsupervised learning.
● Unsupervised learning
Also called the Clustering Algorithm, this type of learning is implemented using an unlabeled dataset. The dataset will simply contain a mix of images of cars and bicycles, for example, and the system will go through each image, extract the similarities or differences between all images, and group them into clusters (“clustering algorithm”). Since an unsupervised learning system doesn’t have a training set from which to learn, it is less accurate than supervised learning.
The process of AI image recognition systems consists of three main steps:
Training. The algorithm is first taught, using a training dataset, what to expect from the input data. The training dataset could be videos, pictures, photos, or other. Training is needed for the neural network to create a perception of how certain classes look. It automatically learns the most important features relevant to the input images until it can accurately decipher between the different classes of visual data. Thus, if one wants a system to identify different types of animals, one will need a dataset with various animal images and photos. If one wants neural networks to recognize different poses, one will need data input that captures various human poses, and so on.
There are multiple well-tested frameworks that are widely used for AI image recognition training today such as Tensorflow, Keras, PyTorch, MxNet, and Chainer.
Testing. After the system completes training with the prepared dataset, it is tested with the images that are not part of the training dataset. This is done to evaluate the performance, accuracy, and usability of the model. The period of testing depends on the quality and quantity of data used for training. If the results are not good enough, steps 1 and 2 are repeated until the accuracy of the model is acceptable. Only after the system reaches the expected accuracy level can it be used to work with real data.
Predicting. Once the system reaches an accuracy level that meets the requirements, it can be used to make predictions based on real data. Prediction is the final stage of the process.
● Facial recognition
Face recognition techniques use deep neural networks trained to identify faces in images or videos. They allow the system to identify a person, recognizing their intentions, emotions, age, ethnicity, and even health state. Face recognition technology is mostly used for security applications, for example, to unlock phones, access buildings, or verify bank transfers.
The system is taught to understand the images routinely acquired throughout the course of treatment. Here, the unsupervised learning system can be extremely helpful because it can pick up on patterns and abnormalities that supervised learning might miss. AI Image recognition was proven to be helpful in the identification of melanoma (a deadly skin cancer) and in monitoring tumors to detect abnormalities in breast cancer scans.
● Retail and E-commerce
AI Image Recognition helps make more accurate product recommendations for virtual stores. When one is looking for a particular item, the system will automatically recommend similar products. Thereby, AI Image Recognition improves the shopping experience and shortens the customer journey.
Search by image is another popular feature based on AI Image Recognition. Online shoppers can easily find a desired item by simply uploading an image of it.
Also, AI technology allows automatic product attribute generation, so customers can easily find what they are looking for based on an attribute (for example, color).
● AI Image recognition for animal monitoring
AI Image recognition software is used for animal monitoring in farming. It allows remote monitoring of livestock for disease detection, compliance with animal welfare guidelines, industrial automation, and more.
● Text recognition
Supervised learning algorithms can be used to identify and extract text from images or videos. Optical Character Recognition (OCR) systems can convert written text into machine-readable form, making it possible to extract number plates from photos and videos and scan documents with AI.
● Visual inspection
This type of AI image recognition is widely used in industrial manufacturing to inspect products on the production line. Here the system is trained to distinguish between defective and non-defective items using labeled training data.
One of the factors of business success is the ability to accept and utilize the results of technological progress. The more resistant one is to new technologies, the less competitive one’s business. AI Image Recognition Technology has scores of benefits. Here are a few.
● Improved customer experience
Now is the era of impatient customers. The rapid pace of life makes service speed one of the most important features deciding whether a customer will continue using a service. It should be accurate and easy to use.
AIIR ID is an AI-based Solution that makes the process of identity verification easy and fast. Optical character recognition technology allows fast and accurate extraction of text information from an ID/Passport photo, and similarity index-based Automatic approval makes the process of authentication fast and easy.
If identification is done by taking a live photo of the face of the user, a cross-examination-based manual approval process will be run.
AIIR ID has an anti-spoofing feature, so users will never be disappointed by service security and accuracy level.
● Work efficiency and accuracy
Whether developing small business or a large corporation, you can achieve higher levels of efficiency and accuracy with AI-based technologies.
For example, our solution for access control - AIIR Pass - allows automation of the visitor and worker management process. You need to register your employees only once; all other work, such as attendance, working hours calculation, granting access to specific areas, and gate interlocking is managed by ALCHERA 's system. You may check the organized data through a simple user-friendly dashboard.
There is no longer a need to hire massive state-of-security managers to spend long hours managing data – Only a few managers, who will manually check the system, are needed.
● Highest level of security
Another great solution by ALCHERA - AIIR Scout - allows monitoring and analysis of video data from existing CCTV cameras for the presence of potential security threats. If there are any abnormalities, the system will automatically send a notification which allows for immediate response.
AIIR Scout not only protects you from unauthorized access, but also allows you to protect your employees with the abnormal behavior detection feature. In case of a safety accident, a falling object, or an emergency medical treatment, the exact location can be pinpointed so that the situation can be handled immediately.
Still not convinced of the benefits brought by AI technology implementation? Contact our team of professionals. Let us analyze your needs and select the perfect AI solution for your business.