{'fraction_non_alphanumeric': 0.022834116856950974, 'fraction_numerical': 0.000671591672263264, 'mean_word_length': 5.622222222222222, '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}}
Unconstrained ear detection using ensemble‐based convolutional neural network model

This paper presents a technique for ear detection from 2D profile face images that is capable of significantly reducing the false positives. In an ear biometrics system, recognition performance highly depends on the performance of the ear detection module. The trade‐off between the complexity and the false positive detection is one of the essential component, where the complexity of a system increases proportionally to achieve a zero false positive rate detection. In literature, available ear detection techniques based on handcrafted features face challenges with low‐quality acquired images affected by illumination, occlusion, and pose variations. We propose an ear detection technique using ensemble of convolutional neural network (CNN). The first part of the technique trains three models of CNN on a given dataset, whereas in later part, weighted average of the outputs of trained models is utilized to detect the ear regions. The used ensemble models show better performance as compared to the case when each individual model is used standalone. The proposed technique is being evaluated on two databases, viz, IIT Indore‐Collection A (IIT‐Col A) database and annotated web ear (AWE) database. Experimental results of ear detection demonstrate the superior performance of the proposed technique over other state‐of‐the‐art techniques in handling illumination, occlusion, and pose variations.