Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly bene...Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly benefits several face analysis tasks,including ethnicity and race classification.We propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation model.For training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and mouth.The DCNN model developed in the first phase was used to create segmentation results.The probabilistic classification method is used,and probability maps(PMs)are created for each semantic class.We investigated five salient facial features from among seven that help in race classification.Features are extracted from the PMs of five classes,and a new model is trained based on the DCNN.We assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.展开更多
基金This work was partially supported by a National Research Foundation of Korea(NRF)grant(No.2019R1F1A1062237)under the ITRC(Information Technology Research Center)support program(IITP-2021-2018-0-01431)supervised by the IITP(Institute for Information and Communications Technology Planning and Evaluation)funded by the Ministry of Science and ICT(MSIT),Korea.
文摘Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly benefits several face analysis tasks,including ethnicity and race classification.We propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation model.For training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and mouth.The DCNN model developed in the first phase was used to create segmentation results.The probabilistic classification method is used,and probability maps(PMs)are created for each semantic class.We investigated five salient facial features from among seven that help in race classification.Features are extracted from the PMs of five classes,and a new model is trained based on the DCNN.We assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.
