儿童面部识别的纵向评估.pdf

1、Longitudinal Evaluation of Child Face RecognitionSurendra Singh(Clarkson University),Stephanie Schuckers(UNC Charlotte)1Challenge in child face recognition due to non-linear cranial growthDeep Neural Network(DNN)models for adults may not always be applicable to children.ObjectiveAnalyze DNN performa

2、nce on the YFA(young face aging)dataset(age up to 8 years).Study specific changes in face features,e.g.,nose,mouth and eyes.Identify unique physiological factors contributing to childrens facial development.Enhance accuracy and effectiveness of face recognition(FR)systems for children.ValueUnderstan

3、ding Challenges in Child Face Recognition.Benchmarking FR Performance Based on Growth in Children.Discovering Specific Changes in Facial Features Impacting Matching Performance.Problem2YFA Database(Young Face Aging)The Young Face Aging(YFA)DatabaseNumber of subjects at each age in the YFA Database.N

4、umber of images at each age in the YFA Database.Contains face images of children aged 3-18 years.330 subjects,with an average of six collections per subject over eight years.Images of the same subject were collected every six months for eight years.The first collection image was used for enrollment

5、and verified against each subsequent collection over the eight-year period.The database includes 60 subjects with a total of 1,322 samples collected over eight years.Collected in a controlled environment with consistent indoor lighting,neutral expressions,and minimized pose variations.Manual annotat

6、ion to exclude extremely blurry images and challenging poses.3Prior work DatabaseLongest time gap Time intervalAccuracyModelECLF113 years6 monthsTAR at 0.1%FARFaceNet:84.55 PFE:98.90 ArcFace:99.38 COTS:99.62ITWCC-D112TAR at 0.1%FARFR Model:COTS FR-A:0.676 FR-B:0.598 FR-C:0.463 FR-D:0.434 FR-E:0.759