Are you eligible? Predicting adulthood from face images via Class Specific Mean Autoencoder

• Published in: Pattern recognition letters Vol. 119; pp. 121 - 130
• Main Authors: Singh, Maneet, Nagpal, Shruti, Vatsa, Mayank, Singh, Richa
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2019; Elsevier Science Ltd
• Subjects: Adulthood prediction; Algorithms; Classification; Face analysis; Feature extraction; Image classification; Machine learning; Supervised autoencoder; Tobacco; Supervised autoencoder; Adulthood prediction; Face analysis
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2018.03.013

•Proposed Class Specific Mean Autoencoder for learning class representative features.•Designed an efficient algorithm for adulthood classification based on facial images, relevant for restricted access.•Proposed Multi-Resolution Face Database of more than 4000 images comprising of 317 subjects, both minors and adults.•Experimental evaluation on two large datasets - Multi-Ethnicity and MORPH dataset.•State-of-the-art results after comparison with existing Deep Learning models and commercial system. Predicting if a person is an adult or a minor has several applications such as inspecting underage driving, preventing purchase of alcohol and tobacco by minors, and granting restricted access. The challenging nature of this problem arises due to the complex and unique physiological changes that are observed with age progression. This paper presents a novel deep learning based formulation, termed as Class Specific Mean Autoencoder, to learn the intra-class similarity and extract class-specific features. We propose that the feature of a particular class if brought similar/closer to the mean feature of that class can help in learning class-specific representations. The proposed formulation is applied for the task of adulthood classification which predicts whether the given face image is of an adult or not. Experiments are performed on two large databases and the results show that the proposed algorithm yields higher classification accuracy compared to existing algorithms and a Commercial-Off-The-Shelf system.