{'fraction_non_alphanumeric': 0.019975031210986267, 'fraction_numerical': 0.0, 'mean_word_length': 5.365079365079365, '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}}
Predicting Occupation from Single Facial Images

Facial images embed age, gender, and other rich information that is implicitly related to occupation. In this work, we advocate that occupation prediction from a single facial image is a doable research direction. We first extract visual features from multiple levels of patches and describe them by locality-constrained linear coding. To avoid the curse of dimensionality and over fitting, a boost strategy called multi-feature SVM is used to integrate features. Intra-class and inter-class visual variations are jointly considered in the boosting framework to further improve performance. In the evaluation, we verify that this is a promising research topic with encouraging performance, and also discuss interesting issues from various perspectives.