Age Estimation From Facial Parts Using Compact Multi-Stream Convolutional Neural Networks

Age is a very useful property in the characterization of individuals, since it is an inherent biological attribute and plays a key role in many real-world applications such as preventing purchase of alcohol and tobacco by minors, human-computer interaction, soft biometrics, electronic customer relationship and as age synthesis in Forensic Art to find lost people. The aging process is influenced by external (health, lifestyle, smoking) and internal (genetics, gender) factors, which makes its estimation difficult for humans, and even more difficult for machines. In this work, we present and evaluate an age estimation approach in unconstrained images using facial parts (eyebrows, eyes, nose and mouth), cropped from the input images using landmarks, to feed a compact multi-stream convolutional neural network (CNN) architecture. Experimental results obtained in the challenging Adience benchmark with real-world images labeled with their respective age groups show that our method is competitive with the literature, even with a significantly smaller CNN and lower computational cost.