{'fraction_non_alphanumeric': 0.019955654101995565, 'fraction_numerical': 0.0033259423503325942, 'mean_word_length': 5.184931506849315, '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}}
A new biologically inspired active appearance model for face age estimation by using local ordinal ranking

In this paper, a new facial feature called Biologically Inspired Active Appearance Model (BIAAM) is proposed for face age estimation by using a novel age function learning algorithm, called Local Ordinal Ranking (LOR). In BIAAM, appearance variations are encoded by extracting Bio Inspired Feature from normalized shape-free images with a mean shape mask. The proposed LOR divides the training set into several groups according to age labels and applies Ordinal Hyperplanes Ranker for each group to determine the final predicting age. A multiple linear regression function is used to decide which group a query sample belongs to. Experimental evaluation on the FG-NET aging database with mean absolute error 4.18 years demonstrates that our method outperforms other state-of-the-art algorithms.