{'fraction_non_alphanumeric': 0.023880597014925373, 'fraction_numerical': 0.0, 'mean_word_length': 4.815028901734104, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Local age group modeling in unconstrained face images for facial age classification

Age classification in the real world is a very challenging task due to the large variation of face appearances (e.g., a variety of human races, genders, facial expressions, poses etc.). In this paper, we propose a new age classification method using local modeling of age group to deal with large variation problem. The local modeling is built by clustering training faces within an age group. Nearest face clusters in the local modeling to a test face contribute in determining the age group of the test face. This enables us to reduce the effect of the variation unrelated to age. For comparing the test face with the face clusters, we combine two complementary similarities that consider the cluster centroid and the intra-cluster variation. Experimental result on a real-world dataset shows that our local modeling based approach is superior to global modeling based approach, achieving state-of-the-art performance.