{'fraction_non_alphanumeric': 0.014084507042253521, 'fraction_numerical': 0.0016570008285004142, 'mean_word_length': 5.391534391534392, '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}}
Multiresolution Face Recognition through Virtual Faces Generation Using a Single Image for One Person

In recent years, various studies have been conducted to provide a real-time service based on face recognition in Internet of things environments such as in a smart home environment. In particular, face recognition in a network-based surveillance camera environment can significantly change the performance or utilization of face recognition technology because the size of image information to be transmitted varies depending on the communication capabilities. In this paper, we propose a multiresolution face recognition method that uses virtual facial images by distance as learning to solve the problem of low recognition rate caused by communication, camera, and distance change. Face images for each virtual distance are generated through clarity and image degradation for each resolution, using a single high-resolution face image. The proposed method achieved a performance that was 5.9% more accurate than methods using MPCA and SVM, when LDA and the Euclidean distance were employed for a DB that was configured using faces that were acquired from the real environments of five different streets.