![]() ![]() An image that received a similarity score of 95% for instance, would indicate that amongst all the faces Rekogniton analyzed, this image had a 95% similarity with the face being searched for. These detected attributes become increasingly useful for customers that need to organize or search through millions of images in seconds using metadata tags (e.g., happy, glasses, age range) or to identify a person (i.e., facial recognition using either a source image or a unique identifier).Ī similarity score is a statistical measure of how likely two faces in an image are the same person, when analyzed by Amazon Rekognition. ![]() For example, Amazon Rekognition can analyze attributes such as eyes open or closed, mood, hair color, as well as the visual geometry of a face. ![]() To understand these emerging capabilities, let’s first discuss how facial recognition works.įacial analysis capabilities, such as those available in Amazon Rekognition, allow users to understand where faces exist in an image or video, as well as what attributes those faces have. This technology has been around for decades, but its usage has become more noticeable, and accessible, in the past few years as it now powers innovative solutions, such as personal photo applications and secondary authentication for mobile devices. Facial recognition is a system built to identify a person from an image or video. ![]()
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