Currently, mainstream high-precision sensors are primarily designed around three core objectives: enhancing image details, resisting light interference, and increasing dimensional information. They can be specifically categorized as follows.

Core high-precision sensor types: These are the most fundamental and core sensors for facial recognition. They provide texture details (such as pores, wrinkles, moles, etc.) by capturing high-definition facial images, forming the premise for high-precision recognition. The resolution is usually above 2 megapixels (1080P), with mainstream choices ranging from 4 to 8 megapixels; some high-end models support 12 megapixels, capturing even finer features. They are compatible with mature 2D facial recognition algorithms, cost relatively controllable, and are widely used in access control, attendance, and mobile phone unlocking scenarios. Representative models include Sony IMX586 (4 megapixels), Samsung S5K3P9 (13 megapixels, excellent low-light performance), and OmniVision OV50A (50 megapixels, high-end security applications).

Core selection criteria for high-precision sensors: Resolution: The higher the image sensor resolution, the richer the details, especially in 2D recognition scenarios; 3D sensor resolution (such as depth map resolution) should be ≥VGA (640×480) to ensure depth accuracy. Signal-to-noise ratio (SNR): The higher the value, the less image noise the sensor produces in low-light/low-reflectivity environments, preventing loss of key features due to noise. Frame rate (FPS): Dynamic facial recognition requires ≥15FPS to prevent lag when the subject is moving quickly; static recognition (e.g., access control) requires ≥5FPS. Distortion rate: Lens distortion should be ≤1% to avoid deformation of facial contours, which may cause algorithm misjudgment, especially in high-precision attendance and payment scenarios.