Here are specifics regarding how Amazon Rekognition processes and returns face attributes.
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FaceDetail Object: For each detected face, a FaceDetail object is returned. This FaceDetail contains data on face landmarks, quality, pose, and more.
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Attribute Predictions: Attributes like emotion, gender, age, and others are predicted. A confidence level is assigned for each prediction, and the predictions are returned with the respective confidence score. A 99% confidence threshold is recommended for sensitive use cases. For age estimation, the midpoint of the predicted age range offers the best approximation.
Note that gender and emotion predictions are based on physical appearance and should not be used for determining actual gender identity or emotional state. A gender binary (male/female) prediction is based on the physical appearance of a face in a particular image. It doesn't indicate a person’s gender identity, and you shouldn't use Rekognition to make such a determination. We don't recommend using gender binary predictions to make decisions that impact an individual's rights, privacy, or access to services. Similarly, a prediction of an emotional doesn't indicate a person’s actual internal emotional state, and you shouldn't use Rekognition to make such a determination. A person pretending to have a happy face in a picture might look happy, but might not be experiencing happiness.
Application and Use Cases
Here are some practical applications and use cases for these attributes:
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Applications: Attributes like Smile, Pose, and Sharpness can be utilized for selecting profile pictures or estimating demographics anonymously.
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Common Use Cases: Social media applications and demographic estimation at events or retail stores are typical examples.
For more detailed information about each attribute, see FaceDetail.
