recognition vs classification
The field of recognition or pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes. However, pattern recognition is a more general problem that encompasses other types of output as well, for example, regression.
大意就是：
識別是對數據（比如圖像）進行尋找規律、抽取特征，然后應用所得到的規律和特征實現某些目的（如分類、回歸）的過程。所以分類只是識別的一個具體例子。
圖像識別的定義
圖像識別，是指利用計算機對圖像進行處理、分析和理解，以識別各種不同模式的目標和對象的技術。
Image recognition is the ability of a computer powered camera to identify and detect objects or features in a digital image or video. It is a method for capturing, processing, examining, and sympathizing images.
Image recognition technology works by detecting salient regions, which are portions that contain the most information about the image or the object. It does this by isolating the most informative portions or features in a selected image and localizes them, while ignoring the rest of the features that may not be of much interest. （摘自Image Recognition – What is Image Recognition? | Sightcorp）
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Image recognition, a subcategory of Computer Vision and Artificial Intelligence, represents a set of methods for detecting and analyzing images to enable the automation of a specific task. It is a technology that is capable of identifying places, people, objects and many other types of elements within an image, and drawing conclusions from them by analyzing them.
Photo or video recognition can be performed at different degrees of accuracy, depending on the type of information or concept required. Indeed, a model or algorithm is capable of detecting a specific element, just as it can simply assign an image to a large category.
So there are different “tasks” that image recognition can perform:
- Classification. It is the identification of the “class”, i.e. the category to which an image belongs. An image can have only one class.
- Tagging. It is also a classification task but with a higher degree of accuracy. It can recognize the presence of several concepts or objects within an image. One or more tags can therefore be assigned to a particular image.
- Detection. This is necessary when you want to locate an object in an image. Once the object is located, a bounding box is placed around the object in question.
- Segmentation. This is also a detection task. Segmentation can locate an element on an image to the nearest pixel. For some cases, it is necessary to be extremely precise, as for the development of autonomous cars.
（摘自Image Recognition : A Complete Guide - Deepomatic）
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人臉識別包含5個步驟：圖像采集，人臉檢測，圖像預處理，特征提取，分析比對。