{'fraction_non_alphanumeric': 0.01829924650161464, 'fraction_numerical': 0.0, 'mean_word_length': 5.158940397350993, '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}}
Age and gender classification using local appearance descriptors from facial components

Face analysis and recognition systems have shown to be a valuable tool for forensic examiners. Particularly, the automatic estimation of age and gender from face images, can be useful in a wide range of forensic applications. In this work we propose to use a local appearance descriptor in a component-based way, to classify age and gender from face images. We subdivide a face image into regions of interest based on automatically detected landmarks, and represent them by using Histograms of Oriented Gradient (HOG). The representations obtained from different face regions are feeded to Support Vector Machine (SVM) classifiers to estimate the age and gender of the person in the image. Experimental analysis show the good results of this component-based approach, and its additional benefits when face images are affected by occlusions.