Abstract
Age as one of the facial attributes plays a significant role in surveillance, web content filtering in electronic customer relationship management, face recognition, among others. The process of ageing changes the colour and texture of skin, as well as the facial skeleton lines with other additional attributes. This process is burdened by such features as ethnicity, gender, emotion, illumination, pose, makeup, and other artifacts that make the task of age estimation non-trivial. One of the promising approaches is to consider the problem as multi-task in order to improve accuracy. We propose a new multi-task CNN for identity verification in real-time access systems. It includes a ranking sub-CNN that groups facial images into predefined age ranges and two sub-CNNs that binary gender classification and multi-class facial expression classification. Even a small number of additional attributes shows more accurate estimates of the age grouping. We have tested our multi-task CNN using five public datasets with promising results.
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Favorskaya, M.N., Pakhirka, A.I. (2023). Age-Group Estimation of Facial Images Using Multi-task Ranking CNN. In: Czarnowski, I., Howlett, R., Jain, L.C. (eds) Intelligent Decision Technologies. KESIDT 2023. Smart Innovation, Systems and Technologies, vol 352. Springer, Singapore. https://doi.org/10.1007/978-981-99-2969-6_13
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DOI: https://doi.org/10.1007/978-981-99-2969-6_13
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