Image Quality

overview the imagequality section of the api response assesses the quality of images submitted as part of the identification document verification process this evaluation plays a crucial role in ensuring that the images are suitable for all subsequent checks like text extraction, security feature verification, and face recognition explanation of values the image meets the quality criteria for the specific check the image does not meet the quality criteria for the specific check the check was not applicable or could not be performed due to image conditions image quality checks below is the detailed breakdown of each image quality check parameter for both the front and back id images parameter sub parameter data type possible values description imagequality result boolean true , false overall result of the image quality checks frontidcheck object container for quality checks on the front id image backidcheck object container for quality checks on the back id image frontidcheck / backidcheck imageglares boolean true , false , none indicates presence of glare on the image imagefocus boolean true , false , none checks if the image is in focus imageresolution boolean true , false , none verifies if image resolution meets the minimum threshold imagecolorness string true , false , none , "na" determines if the image is colorless perspective boolean true , false , none detects excessive perspective distortion bounds boolean true , false , none checks if the document is fully visible within the image bounds portrait string true , false , none verifies the presence and quality of the portrait in the image brightness string true , false , none , "na" assesses if the image's brightness is adequate usage and implications these image quality checks ensure that the images provided are of sufficient quality for accurate processing and analysis poor image quality can lead to failures in extracting critical information, which might affect the overall reliability of the document verification process the results from these checks help identify issues like blurred images, glare effects, improper cropping, or inadequate lighting, which could otherwise compromise the integrity of the verification process example when processing an identification document, if the imagefocus returns false under frontidcheck , it indicates that the front image of the id is not sufficiently focused, which might necessitate a request for a new image submission from the user this helps maintain a high standard of verification accuracy and reduces the risk of erroneous interpretations