{'fraction_non_alphanumeric': 0.02023809523809524, 'fraction_numerical': 0.005952380952380952, 'mean_word_length': 5.229629629629629, '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}}
3D Face Recognition

This work is about biometric 3D face recognition. General biometric systems as well as specific techniques used in 2D and 3D face recognition are described. An automatic modular 3D face recognition method is proposed. First, facial landmarks are located on the face surface and the three dimensional model is aligned to a predefined position. After that, the input probe scan is compared to the gallery template. There are three fundamental face recognition algorithms employed during this process - the eigenface method (PCA), the recognition using histogram-based features, and the recognition based on the anatomical-Bertillon face features. The decision module fuses the scores provided by the utilized recognition techniques finally. The resulting performance is better than any of utilized recognition algorithms.