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Predicting if a person is an adult or a minor has several applications suchas inspecting underage driving, preventing purchase of alcohol and tobacco byminors, and granting restricted access. The challenging nature of this problemarises due to the complex and unique physiological changes that are observedwith age progression. This paper presents a novel deep learning basedformulation, termed as Class Specific Mean Autoencoder, to learn theintra-class similarity and extract class-specific features. We propose that thefeature of a particular class if brought similar/closer to the mean feature ofthat class can help in learning class-specific representations. The proposedformulation is applied for the task of adulthood classification which predictswhether the given face image is of an adult or not. Experiments are performedon two large databases and the results show that the proposed algorithm yieldshigher classification accuracy compared to existing algorithms and aCommercial-Off-The-Shelf system.

Are you eligible? Predicting adulthood from face images via class specific mean autoencoder