Image requirements

The image requirements to use our services are:

Condition	Requirement
Quantity	Only one single face in the image.
Image type	Yoti only accepts JPEGs (95 to 100 quality) and PNGs. Please avoid image format conversion to avoid degrading the image quality. Multiple changes will introduce quality losses in the image.
Image resolution	The recommended minimum resolution is 1280 x 720px. The maximum resolution accepted is FullHD 1920 x 1080px.
Colour	The image must be colour (RGB). Yoti does not accept black and white or filtered images.
Size	The minimum image size is 50KB. The maximum image size is 1.5 MB.

There’s a tradeoff between estimation accuracy and camera quality / light conditions, false-negative errors such as face not detected or spoofing attempt detected may be higher than expected due to one or more of the following factors:

Condition	Description
Lighting	Provide UX and guidance to the user. Jump to User experience for more information.
Face positioning	Guide the users to place their face in the middle of the photo you are capturing. A near frontal pose with no obstruction to the facial features. The minimum inter ocular distance is 120 pixels.
Camera angle	Please try to get a face-on photo of the user to get the best result. The camera’s field of view is too small to provide suitable context around the facial image captured.
Camera type	The API does not work on infrared cameras. To ensure the best performance we are happy to support your testing when evaluating camera options.
Image cropping	Further image cropping can also reduce the context around the facial image captured. This can negatively impact the ability of the algorithm to compare to other ‘normal’ images in its learned model.
Glare / Blur	Insufficient anti-glare capabilities of either the lens or the glass will make it harder for the anti-spoofing to accurately detect edges around faces. This can negatively impact the ability of the algorithm to compare to other ‘normal’ images in its learned model.
Zoom	Optical zoom effects can reduce the context around the facial image captured. This can negatively impact the ability of the algorithm to compare to other ‘normal’ images in its learned model. A distorted lens structure, such as a ‘fish-eye’ lens which is not how the AI model has been trained.
Image compression	Too much image compression is applied to the captured image which creates ‘artefacts’ and inaccuracies in the image (ideally no more than 90% compression). This can negatively impact the ability of the algorithm to compare to other ‘normal’ images in its learned model.

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