Pattern Recognition Guide 2021
Nowadays, pattern recognition is applied in numerous areas of technology dealing with big data. Although the basic components of pattern recognition systems are the same, there is a variety in ways those can be realized and utilized.
Below you will find a brief explanation of the principles and implementations of pattern recognition technology, as well as answers to some common questions.
Table of Contents
- Pattern Recognition Definition
- How Does Pattern Recognition Work?
- Pattern Recognition Approaches
- The Basic Components of Pattern Recognition Systems
- Pattern Recognition Examples
- Pattern Recognition Algorithm for Machine Learning
- Types of Pattern Recognition
- Why Is Pattern Recognition Important?
- Neural Networks for Pattern Recognition
- Other Uses of Pattern Recognition
- What is an example of pattern recognition?
- What are the types of pattern recognition?
- How do humans recognize patterns?
- Is pattern recognition a sign of intelligence
Pattern Recognition Definition
Pattern recognition, simply put, is a process in which a machine finds and recognizes regularities (patterns) in data by applying machine learning algorithms. It can be seen as a kind of highly developed data classification.
How Does Pattern Recognition Work?
Pattern recognition is a complex process of analyzing the input data, extracting patterns, comparing them with certain standards, and using the results to guide the future actions of the system.
Therefore, a pattern recognizer has to possess a diversified set of functions. Recognizing familiar patterns automatically is a basic one. However, in many cases, to function properly, the system should be able to recognize unfamiliar objects and classify them, as well as recognize objects even when the data about them may seem insufficient.
Pattern Recognition Approaches
There are three basic approaches that pattern recognition algorithms utilize:
- Statistical. This approach is based on statistical decision theory. Pattern recognizer extracts quantitative features from the data along with the multiple samples and compares those features. However, it does not touch upon how those features are related to each other.
- Structural (a.k.a. syntactic). This approach is closer to how human perception works. It extracts morphological features from one data sample and checks how those are connected and related.
- Neural. In this approach, artificial neural networks are utilized. Compared to the ones mentioned above, it allows more flexibility in learning and is the closest to natural intelligence.
The Basic Components of Pattern Recognition Systems
Every machine learning-based pattern recognition algorithm includes the following steps.
- Input of data. Large amounts of data enter the system through different sensors.
- Preprocessing or segmentation. At this stage, the system groups the input data to prepare the sets for future analysis.
- Feature selection (extraction). The system searches for and determines the distinguishing traits of the prepared sets of data.
- Classification. Based on the features detected in the previous step, data is assigned a class (or cluster), or predicted values are calculated (in the case of regression algorithms).
- Postprocessing. According to the outcome of the recognition, the system performs future actions.
Pattern Recognition Examples
Examples of pattern recognition can be easily found in nature, like humans recognizing faces or pets responding to their names. In technology, pattern recognition algorithms trained through machine learning are applied in various fields, ranging from everyday tasks to highly specialized areas.
Here are some typical examples:
- NLP (natural language processing): virtual assistants, speech-to-text interfaces, automatic captioning;
- OCR scanners (optical character recognition): mobile scanner apps;
- Medical diagnostic software;
- Meteorological forecast software;
- NIDS (Network intrusion detection systems): security systems, which recognize patterns of suspicious activities.
Pattern Recognition Algorithm for Machine Learning
Pattern recognition algorithms are inseparable from machine learning. When training the pattern recognizer, supervised and unsupervised learning approaches are commonly utilized.
In supervised machine learning, the human participant prepares representative sets of data (referred to as training sets) designed to illustrate the patterns which the system is expected to learn to recognize. After processing those sets, the system's performance is checked by exposing it to other data of a similar format, organized in so-called test sets.
The kind of pattern recognition trained this way is referred to as classification.
When machine learning is unsupervised, the involvement of a human component and pre-existing patterns is reduced to a minimum. In this case, the algorithm is trained to detect new patterns without using any already existing labels, just by being introduced to the large sets of data. Hierarchical or k-means clustering algorithms are often used in this approach. Consequently, the pattern recognition obtained through this type of learning is referred to as clustering.
Alongside machine learning, deep learning is also implemented in training pattern recognizers when it comes to neural networks.
Types of Pattern Recognition
This type of pattern recognition enables identifying particular objects depicted in images. Image recognition is a core part of computer vision, which is basically the ability of a machine to recognize images and take corresponding actions (e.g., a self-driving car slowing down after identifying a pedestrian ahead).
Image recognition is commonly implemented in such processes as:
- Visual search;
- OCR (optical character recognition);
- Face detection.
This type of pattern recognition is used for identifying various sounds. By analyzing audio signals, the system labels them as belonging to a certain category. Here are some examples where sound pattern recognition can be used:
- Surveillance alarm detection;
- Identifying animal species;
- Melody recognition.
This type of pattern recognition analyzes the sounds of a human voice to identify the speaker. Unlike speech recognition, it does not involve language processing and solely spots personal characteristics in a speaking pattern. It is used mostly for security purposes (personal identification). Common areas of usage include:
- Mobile or web applications;
- Internet of things.
Much like optical character recognition identifies letters and words on the image, speech recognition captures elements of a language in the sound of a person speaking. For this technology, widespread areas of usage include:
- Voice-to-text converters;
- Auto captioning for videos;
- Virtual assistants.
Why Is Pattern Recognition Important?
Nowadays, pattern recognition serves as a basis for a number of technologies used in everyday life. Face recognition can be one of the most common examples of implementing pattern recognition on a complex level, as it involves processing a large set of visual elements that make a person's face unique.
Face recognition, as well as other biometrics technologies, have already tremendously influenced the process of identity verification and will continue to influence our society.
Besides, pattern recognition is an irreplaceable analytical tool as well. Complex big data analyses, like stock market prediction, business analytics, or medical diagnostics rely on pattern-recognizing algorithms. WIthout seamless pattern recognition, drawing meaningful conclusions from large sets of data would be impossible.
Neural Networks for Pattern Recognition
Using neural networks for pattern recognition is the most flexible approach to the task. These networks are comparatively autonomous in learning to recognize patterns and are capable of constant development and self-organization. Neural networks make it possible to solve tasks that could probably never be solved using only statistical algorithms.
This adaptiveness and functionality make neural networks widely implemented in pattern recognition software. The most popular type in this area is feedforward neural networks, where information moves in one direction only. Those are often used for tasks of speech recognition or identifying objects.
Other Uses of Pattern Recognition
One of the most promising areas for using pattern recognition is that of biometric identification programs. Making identification fast and accurate, it elevates the levels of security in different areas and improves people's experience with various products and services.
That's why at RecFaces, we focus on developing software products that help businesses to increase their performance with the benefits of biometric identification. Our software levels up the functionality of CCTV, helping to regulate access to control systems, supervise the personnel, or use the gym comfortably.
With pattern recognition technologies, we assist in improving the security and quality of management and user experience in educational and medical institutions, industrial facilities, banks, hotels, gyms, and other institutions.
What is an example of pattern recognition?
As an example of natural pattern recognition, one can think about filling in the missing letters in a word. For technology, it could be the camera detecting faces when the photo is being taken or the phone memo app turning voice into text.
What are the types of pattern recognition?
There are three main types of pattern recognition, dependent on the mechanism used for classifying the input data. Those types are: statistical, structural (or syntactic), and neural. Based on the type of processed data, it can be divided into image, sound, voice, and speech pattern recognition.
How do humans recognize patterns?
The human brain is evolutionarily wired to recognize patterns in the surrounding environment. To do so, it constantly matches the current sensory input with the information from the previous experience stored in the long-term memory.
Is pattern recognition a sign of intelligence?
Being a foundation for predicting and making decisions, pattern recognition is one of the main factors that determine the level of general intelligence. The ability to recognize patterns can be regarded as a sign of intelligence, although the exact definition of the latter may vary.