Hsu M.CLu D.-Y.JIAN-JIUN DING2022-04-252022-04-252021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123056175&doi=10.1109%2fICCE-TW52618.2021.9603130&partnerID=40&md5=48e596a2f9883543cb479da51913d2d0https://scholars.lib.ntu.edu.tw/handle/123456789/607206Age estimation is important in computer vision and surveillance systems. In this paper, we apply a two-stage and multi-class learning-based architecture to perform age estimation. First, the concepts of combining loss functions and domain adaptation are introduced to optimize the extracted features. Then, through a dual classification method, the input image is roughly classified into 10 classes. Then, a variety of regression models are integrated to obtain the final prediction results. Different classes apply different regression models. Simulations show that the proposed algorithm outperforms many well-known methods in age estimation. ? 2021 IEEE.Age estimationClassification methodsCombining lossComputer vision systemDomain adaptationEstimation modelsLoss functionsMulti-class learningRegression modellingSurveillance systemsRegression analysisconference paper10.1109/ICCE-TW52618.2021.96031302-s2.0-85123056175