Abstract
Notwithstanding the enhancement obtained in the last decade researches, the recognition of facial attributes is still today a trend. Besides the mere face recognition, the singular face features, like mouth, nose and hair, are considered as soft biometrics; these can be useful for human identification in cases the face is partially occluded, and only some regions are visible. In this paper we propose a model generated by transfer learning approach for the recognition of the face attributes. Also, an unsupervised clustering model is described, which is in charge of dividing and grouping faces based on their characteristics. Furthermore, we show how clusters can be evaluated by a compact summary of them, and how Deep Learning models should be properly trained for attribute prediction tasks.
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Acknowledgment
A special thank goes to the students Luca Anzalone, Marialuisa Trere and Simone Faiella for having conducted the experiments and proposed the model.
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Anzalone, L., Barra, P., Barra, S., Narducci, F., Nappi, M. (2019). Transfer Learning for Facial Attributes Prediction and Clustering. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_9
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DOI: https://doi.org/10.1007/978-981-15-1301-5_9
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