얼굴영상과 얼굴의 텍스처 및 연대기나이 데이터를 이용한 인지나이 예측
Perceived Age Prediction From Face Image, Facial Texture, and Chronological Age
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초록
People of the same chronological age can show different signs of aging. Biological age is an indicator of the degree of biological aging of the body excluding diseases. Perceived age is highly correlated with biological age, which reflects health assessment, and is often used as a clinical indicator of aging. However, there is a lack of objective methods to quantify perceived age. Therefore, this study aimed to propose a novel perceived age esti- mation algorithm. The proposed algorithm consists of two steps. First, the initial perceived age is predicted from a facial image using a convolutional neural network (CNN) ensemble model. In the second step, the final perceived age is estimated by applying a regression algorithm to the predicted gender, predicted BMI, texture features extracted from the facial image, and the predicted perceived age obtained in the first step. Better performance results were obtained by averaging models generated from various base regression models. The averaged model of CatBoost and LightGBM showed a mean absolute error of 2.9614. The proposed method can be used as a health care model to promote self-care.
Abstract
People of the same chronological age can show different signs of aging. Biological age is an indicator of the degree of biological aging of the body excluding diseases. Perceived age is highly correlated with biological age, which reflects health assessment, and is often used as a clinical indicator of aging. However, there is a lack of objective methods to quantify perceived age. Therefore, this study aimed to propose a novel perceived age esti- mation algorithm. The proposed algorithm consists of two steps. First, the initial perceived age is predicted from a facial image using a convolutional neural network (CNN) ensemble model. In the second step, the final perceived age is estimated by applying a regression algorithm to the predicted gender, predicted BMI, texture features extracted from the facial image, and the predicted perceived age obtained in the first step. Better performance results were obtained by averaging models generated from various base regression models. The averaged model of CatBoost and LightGBM showed a mean absolute error of 2.9614. The proposed method can be used as a health care model to promote self-care.
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