The computer vision system is the technology that deals with identifying and detecting the objects of a particular class in digital images and videos. Local feature detection and description play an essential role in many computer vision applications like object detection, object classification, etc. The accuracy of these applications depends on the performance of local feature detectors and descriptors used in the methods. Over the past decades, new algorithms and techniques have been introduced with the development of machine learning and deep learning techniques. The machine learning techniques can lead the work to the next level when sufficient data is provided. Deep learning algorithms can handle a large amount of data efficiently. However, this may raise questions in a researcher’s mind about selecting the best algorithm and best method for a particular application to increase the performance. The selection of the algorithms highly depends on the type of application and amount of data to be handled. This encouraged us to write a comprehensive survey of local image feature detectors and descriptors from state-of-the-art to the recent ones. This paper presents feature detection and description methods in the visible band with their advantages and disadvantages. We also gave an overview of current performance evaluations and benchmark datasets. Besides, the methods and algorithms are described to find the features beyond the visible band. Finally, we concluded the survey with future directions. This survey may help researchers and serve as a reference in the field of the computer vision system.