Pattern Recognition Working, Types, and Applications

Pattern recognition is a data analysis process that uses machine learning algorithms to classify input data into objects, classes, or categories based on recognized patterns, features, or regularities in data. It has several applications in the fields of astronomy, medicine, robotics, and satellite remote sensing, among others.

Pattern recognition involves two primary classification methods:

Pattern recognition is implemented via several approaches. While it is difficult to decide on one particular approach to perform recognition tasks, we’ll discuss six popular methods commonly used by professionals and businesses for pattern recognition.

Methods of Pattern Recognition

This pattern recognition approach uses historical statistical data that learns from patterns and examples. The method collects observations and processes them to define a model. This model then generalizes over the collected observations and applies the rules to new datasets or examples.

Syntactic pattern recognition involves complex patterns that can be identified using a hierarchical approach. Patterns are established based on the way primitives (e.g., letters in a word) interact with each other. An example of this could be how primitives are assembled in words and sentences. Such training samples will enable the development of grammatical rules that demonstrate how sentences will be read in the future.

This method uses artificial neural networks (ANN) and learns from complex and non-linear input/output relations, adapts to data, and detects patterns. The most popular and effective method in neural networks is the feed-forward method. In this method, learning happens by giving feedback to input patterns. This is much like humans learning from their past experiences and mistakes. The ANN-based model is rated as the most expensive pattern recognition method compared to other methods due to the computing resources involved in the process.

Template matching is one of the simplest of all pattern recognition approaches. Here, the similarity between two entities is determined by matching the sample with the reference template. Such methods are typically used in digital image processing, where small sections of an image are matched to a stored template image. Some of its real-world examples include medical image processing, face recognition, and robot navigation.

In the fuzzy approach, a set of patterns are partitioned based on the similarity in the features of the patterns. When the unique features of a pattern are correctly detected, data can be easily classified into that known feature space. Even the human visual system sometimes fails to recognize certain components despite scanning objects for a long time. The same holds true for the digital world, where algorithms cannot figure out the exact nature of an object. Hence, the fuzzy approach aims to classify objects based on several similar features in the detected patterns.

A hybrid approach employs a combination of the above methods to take advantage of all these methods. It employs multiple classifiers to detect patterns where each classifier is trained on a specific feature space. A conclusion is drawn based on the results accumulated from all the classifiers.

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Pattern recognition is applied for data of all types, including image, video, text, and audio. As the pattern recognition model can identify recurring patterns in data, predictions made by such models are quite reliable.

Pattern recognition involves three key steps: analyzing the input data, extracting patterns, and comparing it with the stored data. The process can be further divided into two phases:

These phases can be further subdivided into the following modules:

Data collection is the first step of pattern recognition. The accuracy of recognition is largely dependent on the quality of the datasets. As such, using open-source datasets is preferable and can save time instead of manual data collection processes. Thus, receiving data from the real world kick-starts the recognition process.

Once the data is received as input, the algorithms start the pre-processing step, where data is cleaned, and impurities are fixed to produce comprehensive datasets that yield good predictions. Pre-processing involves data segmentation. For instance, when you look at a group photograph posted by a friend on social media, you realize that you’re familiar with some of the faces in the picture, which attracts your attention. This is what pre-processing means.

Pre-processing is coupled with enhancement. For example, consider you are viewing the same photograph, but it is ten years older. Now, just to be sure that the familiar faces are real, you start comparing their eyes, skin tone, and other physical traits. This is where enhancement happens. It involves a smoothing and normalization process that tries to correct the image from strong variations. As a result, data becomes easy to interpret for the models.

Next, features are extracted from the pre-processed input data. Here, the input data is converted into a feature vector, representing a reduced version of a set of features. This step solves the problem of the high dimensionality of the input dataset. This means that only relevant features are extracted rather than using the entire dataset.

Once the features are extracted, you should select features with the highest potential of delivering accurate results. Upon shortlisting such features, they are sent for further classification.

Extracted features are then compared to a similar pattern stored in the database. Here, learning can happen in a supervised and unsupervised manner. The supervised method has prior knowledge of each pattern category, while unsupervised method learning happens on the fly. As patterns are eventually matched to the stored data, the classification of input data happens.

Classification is followed by a post-processing step, which makes decisions on the best ways to utilize the results to guide the system efficiently. Moreover, it involves analyzing each segment of the identified or classified data to derive further insights. These extracted insights are then implemented in practice for future pattern recognition tasks.

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Pattern recognition uses several tools, such as statistical data analysis, probability, computational geometry, machine learning, and signal processing, to draw inferences from data. As the recognition model is used extensively across industries, its applications vary from computer vision, object detection, and speech and text recognition to radar processing.

Let's look at some prominent areas that incorporate pattern recognition in one way or another.

Pattern Recognition Applications

Today, image recognition tools are employed by security and surveillance systems across sectors. These devices capture and monitor multiple video streams at a time. This helps detect potential intruders. The same image recognition tech is used at business centers, IT firms, and production facilities as face ID systems.

Another corollary of the same application is presented by the ‘emotion detection system.’ Here, pattern recognition is applied to images and video footage to analyze and detect the human emotions of an audience in real-time. The objective of such systems is to identify the mood, sentiment, and intent of users. Thus, deep learning models are used to detect the patterns of facial expressions and body language of people. This data can then be used by organizations to fine-tune their marketing campaigns and thereby improve customer experience.

Another use case of image recognition is that of ‘object detection.’ This is a key tool for visual search applications. In this case, objects within an image or video segment are identified and labeled. It forms the basis of visual search wherein users can search and compare labeled images.

Thanks to digital transformation across industries, image recognition-based AI systems have become extremely popular. According to a recent report by Expert Market Research, the global image recognition market stood at $29.9 billion in 2022 and is predicted to expand at a CAGR of 14.80% between 2023 and 2028.

Recognition algorithms are typically used to identify patterns in text data, which is then used in applications such as text translation, grammar correction, plagiarism detection, etc. Some machine learning-based pattern recognition algorithms are used to classify documents and detect sensitive text passages automatically. This applies to the finance and insurance sectors, where text pattern recognition is used for fraud detection.

Today, almost all smartphones and laptops have a fingerprint identification feature to protect the device from unauthorized access. This is because these smart devices have used pattern analysis to learn the features of your fingerprint and decide whether to allow or deny the user access request.

When observing how earthquakes and other natural calamities disturb the Earth's crust, pattern recognition is an effective tool to study such earthly parameters. For instance, researchers can study seismic records and identify recurring patterns to develop disaster-resilient models that can mitigate seismic effects on time.

Personal assistants and speech-to-text converters are voice and audio recognition systems that run on pattern recognition principles. For instance, Apple's Siri and Samsung's Alexa are tools that perceive and analyze audio and voice signals to understand the meaning of words and phrases and accomplish the associated tasks.

The relevance of pattern recognition in the medical field was highlighted by a recent paper published by Nature Communications in February 2021. It was assumed that COVID-19 affected the older age group more than younger people, and researchers at MIT opined that it was not only due to the aging of the immune system but also due to lung changes that come along with growing age.

The scientific community at MIT studied lung images of the elderly and used pattern recognition to identify a change in the lung patterns of older groups. The study established that aging caused stiffening of the lung tissues and showed different gene expressions than the ones seen in younger individuals.

Such pattern recognition techniques are also used to detect and forecast cancer. For instance, clinical decision support systems (CDSS) use pattern recognition methods to diagnose patients based on their symptoms, while computer-aided detection systems (CAD) assist doctors in interpreting medical images. CAD applications include breast cancer, lung cancer, and so on.

Pattern recognition can be employed on social media platforms as a security tool. It can be used to find offensive posts, detect suspected religious activists, identify criminals, or zero in on tweets that cause civil unrest. It can also be used to identify posts or comments that indicate self-harm and suicidal thoughts.

While social media already generates enormous amounts of data every day, AI can turn this data into actionable information. For example, Facebook is known to employ pattern recognition to detect fake accounts by using an individual's profile pics.

Organizational networks can use pattern recognition-based security systems that detect activity trends and respond to changing user behavior to block potential hackers. If cybersecurity teams have instant access to malware patterns, they can take appropriate action before an attack or threat hits the network. For instance, intrusion detection systems are AI filters that sit inside a corporate network and look for potential threats on the network.

In modern times, robotic task forces have become common across industries. Robots are being increasingly employed to perform dangerous tasks. For instance, the detection of radioactive material is nowadays performed by robots. These machines use pattern recognition to complete the task. In this case, the robot's camera captures images of a mine, extracts the discriminative features, and uses classification algorithms to segregate images into dangerous or non-dangerous based on the detected features.

Optical character recognition OCR converts scanned images of text, photos, and screenshots into editable documents. The character recognition process eliminates the need to write documents manually, saving time and increasing efficiency. For example, PDF document editors and digital libraries refer to such programs with built-in character recognition features.

Coding is another field where pattern recognition is widely used. Pattern recognition assists developers in identifying errors in codes. Some of the popular examples include:

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As pattern recognition applications become more futuristic and intelligent, advanced AI systems are well-placed to fully automate tasks and solve complex analytical problems. While endless possibilities exist as to what such smart AI tools can achieve, the future of pattern recognition lies in the hands of NLP, medical diagnosis, robotics, and computer vision, among others.

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AI Researcher