{'fraction_non_alphanumeric': 0.013064133016627079, 'fraction_numerical': 0.0023752969121140144, 'mean_word_length': 5.2444444444444445, '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}}
Boosting a Biologically Inspired Local Descriptor for Geometry-free Face and Full Multi-view 3D Object Recognition

Object recognition systems relying on local descriptors are increasingly used because of their perceived robustness with respect to occlusions and to global geometrical deformations. Descriptors of this type -based on a set of oriented Gaussian derivative filters -are used in our recognition system. In this paper, we explore a multiview 3D object recognition system that does not use explicit geometrical information. The basic idea is to find discriminant features to describe an object across different views. A boosting procedure is used to select features out of a large feature pool of local features collected from the positive training examples. We describe experiments on face images with excellent recognition rate.