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QualityAssessment

We are working on the implementation of a Face Image Quality Assessment according to ISO/IEC 29794-5.

Message definition

message QualityAssessment {
    string check = 1;
    double score = 2;
    string message = 3;
}

QualityAssessment fields

check
The quality check performed, see below.
score
The outcome of the quality check. This is a score int the range between 0.0 and 1.0. The higher the value, the better the check was passed. Some tests simply return 0.0 or 1.0 here, if the check fails or succeeds respectively.
message
A text with additional info about this quality assessment.

Quality Checks performed

ImageSize
The size of any input image should not be bigger than the recommended image size (1200 x 1600). If an image is bigger than this size in both dimensions, it is automatically scaled down.
This check is only reported in case that the image had to be scaled down and is just for your information.
SuitableFace

The service typically needs to find at least one suitable face in the image. Not all found faces are suitable to be used by a specific API. In case that a found face is not suitable it will be rejected and a quality assessment message is generated containing the reason for the rejection.
This check is only reported in case that a non suitable face was found and is just for your information.
If all found faces have been rejected, an additional FaceNotFound error will be generated of course.

Possible reasons for non suitable faces are:
  • EyesInverted: the eyes are inverted, i.e. the face seems to be upside down.
  • NoseAboveEyes: the nose is above the eyes.
  • (Profile: seems to be a profile face image.)
  • EyesTooCloseTogether: minimum horizontal eye distance not reached.
  • BeyondBorder: at least one of the eyes is too close to the border.
FullyVisibleFace
To crop out the face-area of a found face from an image, there needs to be enough space around the face. Especially if the face is tilted and needs to be rotated, more space around the face is needed.
This check is only reported in case that a face is too close to the edge and cannot be completely cropped.
If no face is fully visible within an image, an additional ThumbnailExtractionFailed error will be generated.

OFIQ Quality Measures

In addition to our custom checks, the BWS 3 implements the Open Source Face Image Quality (OFIQ) library. OFIQ is the reference implementation for the ISO/IEC 29794-5 standard. For more information, see the official OFIQ GitHub Project.

The table below lists the 28 OFIQ quality measures performed by our service:

Category Quality Measure Description Defined in
Unified Score Unified quality score A unified quality score (based on the MagFace50_FP16 CNN model) which aims to predict the utility of the facial image for recognition purposes. It is used to ensure that only facial images of sufficient quality are fed into a face recognition system. ISO/IEC 29794-1:2024, ISO/IEC FDIS 29794-5:2024
Capture related Background uniformity Measures the mean squared lengths of the luminance gradients on the background of the face image. It utilizes a face parsing segmentation map and is mainly relevant for reference images for ID documents. ISO/IEC WD5 29794-5:2022, ISO/IEC 39794-5:2019
Capture related Illumination uniformity Measures the uniformity of lighting across the face by evaluating the intersection of luminance histograms in specific left and right measurement zones on the cheeks. It assesses conformance to requirements to prevent strong shadows that negatively affect recognition utility. ISO/IEC WD5 29794-5:2023, ISO/IEC 39794-5:2019
Capture related Mean luminance Computes the mean luminance (brightness) within the landmarked facial region. It addresses exposure related defects known to negatively affect the reliability of biometric decisions. ISO/IEC CD3 29794-5:2023
Capture related Luminance variance Computes and outputs the Luminance Variance within the landmarked region. It measures the spread of brightness values to identify potential contrast issues. ISO/IEC CD3 29794-5:2023
Capture related Over exposure Determines the proportion of pixels in the non occluded face region with luminance in the range [247; 255]. Over exposure represents a capture related defect that washes out texture details and negatively affects image utility. ISO/IEC DIS 29794-5:2024
Capture related Under exposure Determines the proportion of pixels in the non occluded face region with luminance in the range [0; 25]. It identifies images that are too dark, which can leave textures less visible for biometric recognition. ISO/IEC DIS 29794-5:2024
Capture related Dynamic range Computes the entropy of the normalised luminance histogram in the landmarked region. It estimates the range of brightness levels used, which directly influences recognition performance. ISO/IEC CD3 29794-5:2023
Capture related Sharpness Assesses image sharpness using a Random Forest Classifier trained on Sobel and Laplacian filter outputs. High sharpness is required to ensure that texture details are visible to face recognition algorithms. ISO/IEC FDIS 29794-5:2024
Capture related Compression artifacts Predicts the Peak Signal to Noise Ratio (PSNR) of the input image relative to an uncompressed source using a CNN model. It identifies blocking artifacts resulting from heavy compression (e.g., JPEG, JPEG2000) that may impair recognition performance. ISO/IEC FDIS 29794-5:2024
Capture related Natural colour Evaluates typical skin colour values in the CIELAB colour space within specific measurement zones on the cheeks. It detects unnatural colour casts or saturation which can negatively affect the utility of facial images. ISO/IEC CD1 29794-5:2023, ISO/IEC 39794-5:2019
Subject related Single Face Ensures that exactly one face is detected in the image as required for biometric processing. ISO/IEC FDIS 29794-5:2024
Subject related Eyes open Measures the ratio of the maximal distance between upper and lower lid divided by the distance between eyes’ midpoint and chin. It assesses eye openness, as closed eyes can massively increase face recognition error rates. ISO/IEC CD1 29794-5:2023
Subject related Mouth closed Measures the ratio between the mouth's aperture and the distance between eyes’ midpoint and chin. It ensures conformance to requirements for passport style images where a closed mouth is typically required. ISO/IEC CD1 29794-5:2023
Subject related Eyes visible Computes the proportion of occlusion of the Eyes Visibility region using face occlusion segmentation. It checks if the eye region is free from occlusions, which are defects known to negatively affect recognition decisions. ISO/IEC CD3 29794-5:2023, ISO/IEC 39794-5:2019
Subject related Mouth occlusion Computes the proportion of occlusion of the mouth region using landmark based segmentation. This specific component is used to detect defects like face masks or hair in the mouth area that hinder recognition. ISO/IEC CD1 29794-5:2023
Subject related Face occlusion Measures the proportion of the landmarked face region marked as occluded in a segmentation map, assessing the overall degree of occlusion by objects or hair to provide transparent, actionable feedback. ISO/IEC CD2 29794-5:2023
Subject related Inter eye distance Estimates the inter-eye distance accounting for perspective projections due to the head pose. This measure evaluates the image resolution, which is essential for ensuring high sample quality. ISO/IEC CD1 29794-5:2023
Subject related Head size Calculates the distance T between the eye midpoint and the chin relative to the image height. It helps ensure the face size meets standard geometric requirements for ID and reference images. ISO/IEC FDIS 29794-5:2024
Subject related Crop of the face: leftward Assesses the leftward crop measure based on the position of the right eye center relative to the inter-eye distance, ensuring sufficient margin exists on the left side of the face. ISO/IEC FDIS 29794-5:2024
Subject related Crop of the face: rightward Assesses the rightward crop measure based on the position of the left eye center relative to the inter-eye distance and image width, ensuring sufficient margin exists on the right side. ISO/IEC FDIS 29794-5:2024
Subject related Crop of the face: upward Assesses the upward crop measure based on the distance from the eyes' midpoint to the upper image border, relative to the vertical face size (distance between eyes and chin). ISO/IEC FDIS 29794-5:2024
Subject related Crop of the face: downward Assesses the downward crop measure based on the distance from the chin to the lower image border, relative to the vertical face size. ISO/IEC FDIS 29794-5:2024
Subject related Head pose: yaw Estimates the head rotation around the vertical axis (yaw angle) using a 3DDFA-V2 CNN model. A value of 0° indicates a perfectly frontal pose, as described in ISO/IEC FDIS 29794-5:2024, clause 7.4.11. ISO/IEC FDIS 29794-5:2024
Subject related Head pose: pitch Estimates the head rotation around the transverse axis (pitch angle) using a 3DDFA-V2 CNN model. A value of 0° indicates a neutral horizontal gaze, as described in ISO/IEC FDIS 29794-5:2024, clause 7.4.11. ISO/IEC FDIS 29794-5:2024
Subject related Head pose: roll Estimates the head rotation around the sagittal axis (roll angle) using a 3DDFA-V2 CNN model. A value of 0° indicates an upright head orientation, as described in ISO/IEC FDIS 29794-5:2024, clause 7.4.11. ISO/IEC FDIS 29794-5:2024
Subject related Expression neutrality Determines whether the facial expression is neutral using a dedicated CNN-based approach (HSEmotionWithTwoCNNs). Non neutral expressions (e.g., smiling, squinting) distort facial topology and reduce the accuracy of face recognition. ISO/IEC FDIS 29794-5:2024
Subject related No head coverings Determines the proportion of pixels in the face parsing segmentation map labeled as "hat" or "clothing" above the eyes. This detects items like hats or veils that might obscure the face, which is relevant for conformance to ID document standards. ISO/IEC CD3 29794-5:2023, ISO/IEC 39794-5:2019