The difference between face capture and face recognition

Core differences

Comparison mode

• 1:1 Verification: Confirm "are you who you claim to be", such as facial recognition access control and payment verification. Fast speed, high accuracy, and the threshold can be set higher.
• 1:N identification: Find out "who you are" among N people, such as blacklist control. The larger N is, the higher the similarity threshold and computing power requirements are, and the greater the risk of false positives.

Factors affecting accuracy

• Face pixels: The recommended distance between the eyes is ≥ 60 pixels. Below 40 pixels, the recognition rate drops sharply.
• Posture: The front face is the best, but the accuracy drops significantly for side faces at large angles (>45°).
• Lighting: Backlight, strong shadows, and no fill light at night will all affect it.
• Occlusion: Masks, hats, and glasses will cause feature loss.
• Quality of the library: Photos of the library must be clear, front-facing, and recent.

Hardware requirements

• Specialized face capture camera (large aperture, wide dynamic range, low illumination), which is different from ordinary surveillance cameras.
• The recommended installation height is 2.2 to 2.8 meters, and the depression angle is ≤ 20° to ensure that the front face can be photographed.
• The width of the passage should be controlled between 1.5 and 2.5 meters to prevent multiple people from entering the picture at the same time.
• The fill light should be soft and even to avoid yin and yang faces.

Privacy and Compliance

• Prior to collection, it should be publicly informed and clear signs should be set up.
• Follow the minimum necessary principle and do not collect in non-essential areas.
• Data is encrypted and stored, access rights are strictly controlled and retrieval logs are retained.
• Clear the retention period and automatically delete it upon expiration.
• When it comes to face collection in public places, you need to pay attention to local regulations and requirements.