Johannes Merkle (secunet)：开源人脸图像质量 (OFIQ).pdf

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1、Johannes Merkle01.04.2025Open Source Face Image Quality(OFIQ)https:/de.wikipedia.org/wiki/StyleGAN2Agenda|OFIQ Objectives Algorithms Release Way Forward3Objectives|OFIQ C+software library for facial image quality assessment(FIQA)Checks quality requirements from ISO/IEC 39794-5:2019 Open source publi

2、shed under liberal licences Commercial use possible,no copy-left Support of many plattforms(incl.mobile devices)Evaluation through NIST FATE Quality SIDD and internal test Reference implementation of upcoming revision of ISO/IEC 29794-5 Development funded by BSI4Pre-Processing Algorithms|OFIQ Face D

3、etection Face Landmark Estimation Alignment Segmentations:Landmarked Region Occlusion Segmentation Face Parsing 5Algorithms|OFIQ Unified Quality Score Background Uniformity Illumination Uniformity Moments of the Luminance Distribution Under-Exposure/Over-Exposure Dynamic Range Sharpness No Compressi

4、on Artifacts Natural Colour Single Face Present Eyes Open Mouth Closed Eyes Visible Mouth Occlusion Prevention Face Occlusion Prevention Inter-Eye Distance Head Size Crop of the Face Head Pose Expression Neutrality No Head Coverings6Algorithms-Unified Quality Score|OFIQNot limited to certain quality

5、 defectsCNN MagFace(iResNet 50 model)Excellent results in FATE Quality 1st out of 52 algorithms1Good prediction of face recognition scores1Measured by FNMR after removal of 5%lowest quality images7Algorithms-Sharpness|OFIQRandom Forest classifier Several features:Sobel-Filter Laplace filter Differen

6、ce of image from mean-filtered imageRestricted to landmarked region Trained on synthetic and real blur8Algorithms-SharpnessGood results in FATE Quality 5th out of 34 Only synthetic blur Internal evaluation on FRGCv2(real blur)Accuracy high but not very high Challenging9Algorithms-No Compression Arti