# Overview of face detection and face comparison Amazon Rekognition provides users access to two primary machine learning applications for images containing faces: face detection and face comparison. They empower crucial features like facial analysis and identity verification, making them vital for various applications from security to personal photo organization. **Face Detection** A face detection systems address the question: "Is there a face in this picture?" Key aspects of face detection include: + **Location and orientation**: Determines the presence, location, scale, and orientation of faces in images or video frames. + **Face attributes**: Detects faces regardless of attributes like gender, age, or facial hair. + **Additional Information**: Provides details on face occlusion and eye gaze direction. **Face Comparison** A face comparison systems focus on the question: "Does the face in one image match a face in another image?" Face comparison system functionalities include: + **Face matching predictions**: Compares a face in an image against a face in a provided database to predict matches. + **Face attribute handling**: Handles attributes to compares faces regardless of expression, facial hair, and age. **Confidence scores and missed detections** Both face detection and face comparison systems utilize confidence scores. A confidence score indicates the likelihood of predictions, such as the presence of a face or a match between faces. Higher scores indicate greater likelihood. For instance, 90% confidence suggests a higher probability of a correct detection or match than 60%. If a face detection system doesn’t properly detect a face, or provides a low confidence prediction for an actual face, this is a missed detection/false negative. If the system incorrectly predicts the presence of a face at a high confidence level, this is a false alarm/false positive. Similarly, a facial comparison system may not match two faces which belong to the same person (missed detection/false negative) or may incorrectly predict that two faces from different people are the same person (false alarm/false positive). **Application design and threshold setting** + You can set a threshold that specifies the minimum confidence level required to return a result. Choosing appropriate confidence thresholds is essential for application design and decision-making based on the system outputs. + Your chosen confidence level should reflect your use case. Some examples of use cases and confidence thresholds: + + **Photo Applications**: A lower threshold (e.g., 80%) might suffice for identifying family members in photos. + **High-Stakes Scenarios**: In use cases where the risk of missed detection or false alarm is higher, such as security applications, the system should use a higher confidence level. In such cases, a higher threshold (e.g., 99%) is recommended for accurate facial matches. For more information on setting and understanding confidence thresholds, see [Searching faces in a collection](collections.md).