{'fraction_non_alphanumeric': 0.017013232514177693, 'fraction_numerical': 0.0, 'mean_word_length': 5.61875, '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}}
HafaNet: An Efficient Coarse-to-Fine Facial Landmark Detection Network

Facial landmark detection can be applied in various facial analysis tasks. It is a challenging problem due to the various poses and high real-time requirements. To balance accuracy and computational efficiency, we propose an efficient coarse-to-fine network by combining heatmap regression and lightweight coordinate regression to detect facial landmarks. The heatmap regression network branch employs an efficient spatial pyramid and attention mechanism to regress to a better quality heatmap. The lightweight coordinate regression network branch introduces the local region perceptron to make the small network focus on the region of interest. Our method develops the advantages of heatmap regression and coordinate regression, which can improve landmark detection. Moreover, we propose a novel two-stage FTS loss function that can further effectively solve the outlier problem in facial landmark detection. The comprehensive experiments demonstrate the effectiveness of our approach.