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Human ear detection is one of the key features in biometric-based identification and person authentication. Detecting ears for position, dimension, and texture is tedious due to the height and movement of humans. The biometric systems based on ear detection are defaced by the height of humans and specific positions. For this purpose, a multi-feature and multi-contour-based assessment for precise detection is required. To address this problem, this article introduces an Enhanced Active Contour Method (EACM) for improving ear detection precision. The proposed method extracts the textural features for differentiating the antitragus and helix of the human ear. The extracted features are fused using a fuzzy threshold for the maximum possibilities of the ear structures. In verifying the maximum possibilities, the helix and antitragus contours are fused by identifying similar features. The features from the existing dataset are used for verifying the similarity; the similarity succeeding contours are used for detecting human ears. The proposed method maximizes accuracy and precision by 12.99% and 12.79% and reduces extraction time by 11.34%. [ABSTRACT FROM AUTHOR]

When someone without permission does pre-intrusion activities like auditory surveillance close to a door or window and has oils and waxes on their auricles, the deposition of a latent ear print may take place. By using methods analogous to those used in fingerprint lifting processes, the visibility of this print can be increased. An earprint is a 2D representation of the auricle segments that have come into contact with a certain surface, similar to the impression left by a rubber stamp. This study critically reviewed the efficacy of ear prints as a form of corroborative evidence and its significance in the realm of personal identification. Ear prints, in conjunction with other latent evidence, have the potential to be identified and gathered at the location of a criminal incident. The literature that has been examined exhibits promising advancements and a notable level of precision in the field of ear print development and identification, thereby emphasizing the necessity for further study. [ABSTRACT FROM AUTHOR]

In related work, such as human ear shape clustering, three-dimensional human ear modeling, and personal customized headphones, the key physiological curves of the human ear and the accurate positions of key points need to be determined. Moreover, as an important biological feature, the morphological analysis and classification of the human ear are of considerable value for medical work related to the human ear. However, because of the complex morphological structure of the human ear, the generation of a general standard for the morphological structure of the human ear is difficult. This study divided the morphological structure of the human ear into three regions, namely, helix, antihelix, and concha, for instance segmentation and key physiological curve extraction. Traditional edge extraction methods are sensitive to illumination and posture variations. Moreover, the color distribution of one human ear image is relatively consistent. Thus, the transition among the three regions may not be obvious, which will cause poor adaptability for traditional edge extraction methods when extracting the key physiological curves of the human ear. To address this problem, this study proposed an improved YOLACT（You Only Look At CoefficienTs） instance segmentation model based on the ResNeSt backbone and the “screening mask” strategy, which improves the original YOLACT model from two aspects, namely, localization and segmentation. Our ResNeSt-based YOLACT model was trained with labeled ear images from the USTB-Helloear image set. In the prediction stage, the original cropping mask strategy was discarded and replaced with our proposed screening mask strategy to ensure the integrity of the edges of the segmentation area. These improvements enhance the accuracy of curve detection and extraction and can accurately segment different regions of the human ear and extract key physiological curves. Compared with other methods, our proposed method shows better segmentation accuracy on the test image set and is more robust to posture variations of the human ear.

Introduction- The earlobe is formed by tough areola and adipose tissue, lacking the firmness and elasticity of the rest of the ear auricle. The study of earlobe attachment, as a morphogenetic trait can be used to investigate the diversity that arises within and among different populations due to genetic variations. Materials and methods- The present longitudinal randomized study was carried on randomly selected 100 families, residing in Sriganganagar Rajasthan. Digital camera, USB Cable and laptop were used for the study. Results- Out of total 158 offspring 86.08% are free and 13.92% are attached. Males and females displayed the highest frequency of free earlobes. There is no significant gender difference in the pattern of earlobe attachment. Conclusion- The free earlobe is more predominant than attached earlobe and this study investigated an inheritance pattern of simple dominance recessive pattern where free earlobes are dominant over attached earlobes. [ABSTRACT FROM AUTHOR]

Nowadays, the use of the human ear images gains importance for the sustainability of biometric authorization and surveillance systems. Contemporary studies show that such processes can be done semi-automatically or fully automatically, instead of being done manually. Due to the fact that deep learning uses abstract features (i.e., representation learning), it reaches quite high performance values compared to classical methods. In our study, a synergistic gender recognition approach based on hybrid deep learning was created based on the use of human ear images in classifying people fully automatically according to their gender. By means of hybridization, hybrid deep neural network architectural models are used, which include both convolutional neural network component and recurrent neural network type components together. In these models, long-short term memory and gated recurrent unit are taken as recurrent neural network type components. Thanks to these components, the hybrid model extracts the relational dependencies between the pixel regions in the image very well. On account of this synergistic approach, the gender classification accuracy of hybrid models is higher than the standalone convolutional neural network model in our study. Two different image datasets with gender marking were used in our experiments. The reliability of the experimental results has been proven by objective metrics. In the conducted experiments, the highest values in gender recognition with hybrid models were obtained with the test accuracy of 85.16% for the EarVN dataset and 87.61% for the WPUT dataset, respectively. Discussion and conclusions are included in the last section of our study. [ABSTRACT FROM AUTHOR]

Background The human ear is a distinctive facial feature, revealing valuable information about ethnicity, gender, and age. Anthropometric measures play a crucial role in fields such as forensic medicine, prosthetics, and plastic surgery. The external ear, known for its diversity in structure and individual characteristics, has become a subject of interest in various populations. This study aims to determine the mean values of morphometric measurements for both right and left ears while exploring sexual dimorphism in ear dimensions. Methodology A prospective, observational, cross-sectional study was conducted among 200 volunteers from the eastern province of Saudi Arabia, comprising 98 males and 102 females, at the Department of Anatomy, Institute of Medical Sciences, Imam Abdulrahman Bin Faisal University. Participants were randomly selected from King Fahad University Hospital workers and patients. Using a Vernier caliper and after obtaining consent, various aspects of ear morphology were measured. The study employed statistical analyses such as the volunteers' t-test, Pearson's coefficient of correlation, and linear regression equations. Results In males, the mean total height of the right and left ears was 6.054 ± 0.5394 and 6.044 ± 0.5235 cm, respectively, while for females, it was 5.489 ± 0.4481 and 5.763 ± 4.8446 cm, respectively. The mean widths, heights, and other dimensions of the ears exhibited variations between genders. Conclusions The study provides comprehensive insights into the dimensions and indices of the pinna among the population of the eastern province of Saudi Arabia. The findings confirmed the presence of sexual dimorphism in the ear measurements, consistent with observations in other ethnic groups., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Hiware et al.)