Image recognition is one of the features that is deployed in most modern devices and programs. The feature has a wide range of applications, the most common one being in security systems. It enables devices to recognize the identity of a person or object just by its image. Face recognition is a product of image recognition.
So, what does it take to create an application or a software that has an image recognition feature? You simply need to program it using a programming language. Of course, there are some languages that can do this job. Here are the best programming languages for image recognition.
Contents
Best Programming Language for Image Recognition
1. Matlab
Matlab is an independent programming language that has its own framework and IDE which come in a more robust workspace. The programming language comes with a broad range of tools that you can use to handle more technical programming tasks with ease. Image recognition and face processing are some of the tasks that can be handled by Matlab.
Matlab offers an array of built-in tools for matrix calculation. It is imperative to note that image recognition and matrix calculation go hand in hand. Some of the tools that are available in Matlab can perform complex image processing tasks such as cropping, rotating, masking among others.
There is also a dedicated Matlab code for invoking and implementing face recognition feature. This code uses the AT&T database. For this reason, you should download the AT&T database before using this code. Most advanced face recognition applications are written in Matlab.
2.Python
Currently, Python is rated as the most popular programming language. Its simplicity and versatility are some of the reasons why many programmers prefer to use it. Despite being simple, Python is a language that you can depend on to perform complex tasks. It can be used to create image processing and recognition features.
The popularity of Python has made it be quite resourceful. It is packed with several libraries that are needed for several functionalities, among them-image recognition. One of the most powerful and efficient libraries is the Scikit-Learn Machine Learning library. Apart from image recognition, the libraries can be used for more intelligent functions such as face recognition and motion detection.
2. C/C++/C#
You can never go wrong when it comes to the C family of programming languages. They are powerful and can do anything, including creating image processing and recognition functionalities. The C family of programming languages gives you two options for creating image processing feature. You can choose to code everything from scratch whereby you write the codes manually. The second option is to use the existing libraries that are specially designed for these programming languages. These libraries include OpenGL, EmguCV, OpenCV, and many others. They have an intelligent image processing feature for image recognition.
4. Java
Like C and C++, we can never afford to underestimate the power of Java programming language. This language is powerful enough to perform complex functionalities. It can be used to create applications for image processing and image recognition.
As a standalone programming language, you can create an image processing functionality from scratch. The advantage of this method is you will have the full control of the functionality. Alternatively, there are powerful libraries that can be incorporated into Java for the purpose of creating the image recognition feature. OpenCV is just one of the libraries that is compatible with Java. You can choose whichever method that suits you.
The OpenCV Tool
Open Source Computer Vision is an advanced tool that is used for real-time processing. It is also a cross-platform tool hence can be integrated into any programming language for the purpose of executing image processing and recognition functions. It can be integrated with C, C++, Java, Python and Android programming languages.
OpenCV comes with patent-free algorithms that you can use without any legal restrictions. It can be used for commercial and academic purposes. It has a dedicated Face Recognizer class which you can use to experiment the capabilities of image recognition feature without any hassle. The class is accompanied by an information-rich documentation which will show you how to implement the image recognition feature. In overall, OpenCV is a go-to tool for image recognition regardless of the programming language that you are using.
There are several other programming languages that you can use for developing image recognition functionality. Before you start using any language, learn how to process matrix as it is the building block of image recognition programming.