Automatic age estimation based on facial aging patterns is proposed in this paper. The method, named AGES (AGing pattErn Subspace), models aging patterns by constructing a representative subspace. Each aging pattern is a sequence of face images sorted by time, and the proper aging pattern for a face image is determined by the projection in the subspace that can best reconstruct the face image. The position of the face in the aging pattern indicates its age. AGES is compared with existing methods like WAS and AAS, as well as classification methods like kNN, BP, C4.5, and SVM. It is shown that AGES performs better than these methods and is comparable to human observers. The aging pattern subspace is a global model for aging patterns, each corresponding to a sequence of age labels. Age estimation is based on a single face input and expects a single age output. The method involves finding the proper aging pattern for the input face and determining its position within that pattern. The proper aging pattern is selected by finding the point in the subspace that can best reconstruct the face image. Once the proper aging pattern is determined, the position of the face in the pattern indicates its age. The paper presents experiments using the FG-NET Aging Database and the MORPH database. AGES is compared with other methods, and it is shown to perform better than most existing methods. The results indicate that AGES is not only significantly better than other algorithms but also comparable to human observers. The method is also tested for age range estimation and aging effects simulation. The results show that AGES can simulate aging effects and improve face recognition accuracy. The paper concludes that AGES is an effective method for automatic age estimation and has potential for future improvements in accuracy, especially for children's faces.Automatic age estimation based on facial aging patterns is proposed in this paper. The method, named AGES (AGing pattErn Subspace), models aging patterns by constructing a representative subspace. Each aging pattern is a sequence of face images sorted by time, and the proper aging pattern for a face image is determined by the projection in the subspace that can best reconstruct the face image. The position of the face in the aging pattern indicates its age. AGES is compared with existing methods like WAS and AAS, as well as classification methods like kNN, BP, C4.5, and SVM. It is shown that AGES performs better than these methods and is comparable to human observers. The aging pattern subspace is a global model for aging patterns, each corresponding to a sequence of age labels. Age estimation is based on a single face input and expects a single age output. The method involves finding the proper aging pattern for the input face and determining its position within that pattern. The proper aging pattern is selected by finding the point in the subspace that can best reconstruct the face image. Once the proper aging pattern is determined, the position of the face in the pattern indicates its age. The paper presents experiments using the FG-NET Aging Database and the MORPH database. AGES is compared with other methods, and it is shown to perform better than most existing methods. The results indicate that AGES is not only significantly better than other algorithms but also comparable to human observers. The method is also tested for age range estimation and aging effects simulation. The results show that AGES can simulate aging effects and improve face recognition accuracy. The paper concludes that AGES is an effective method for automatic age estimation and has potential for future improvements in accuracy, especially for children's faces.