A human middle ear consists of an eardrum and three ossicles which are linked by each other, and connect with the eardrum and an inner ear. The inner ear consists of a cochlea and a vestibular system. An abnormality o...A human middle ear consists of an eardrum and three ossicles which are linked by each other, and connect with the eardrum and an inner ear. The inner ear consists of a cochlea and a vestibular system. An abnormality of the human middle ear such as ossicular dislocation may cause conductive hearing loss. The conductive hearing loss is generally treated by surgery using artificial ossicles. The treatments of conductive hearing loss require a better understanding of characteristics and dynamic behaviors of the human middle ear when the sounds transmit from outer inner to inner ear. The purpose of this research is to simulate the dynamic behaviors of a human ear system comprising the middle ear and the cochlea in the inner ear using the finite element method (FEM). Firstly, the eigen-value analysis was performed to obtain the natural frequencies and vibration modes of the total ear system. Secondly, the frequency response analysis was carried out. Thirdly, the time history response analyses were performed using human voices as the external forces. In the time history response analyses, the sounds created as input sound pressures were used. Human voices, for example vowels “I”, “u” and “e” as input sound pressures were created by using the sound pressures downloaded from the opening samples of human voices as wav files in a website. Then it was clarified that the high frequency components of sounds are reduced by the middle ear system.展开更多
Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues.Therefore,biometric systems have emerged as a te...Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues.Therefore,biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics.Among different viable biometric modalities,the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems.In recent years,most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results.Nevertheless,such traditional models can be sensitive to several unconstrained environmental factors.As such,some traits may be difficult to extract automatically but can still be semantically perceived as soft biometrics.This research proposes a new group of semantic features to be used as soft ear biometrics,mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each other.Hence,the research study is focused on the fusion of the soft ear biometric traits with traditional(hard)ear biometric features to investigate their validity and efficacy in augmenting human identification performance.The proposed framework has two subsystems:first,a computer vision-based subsystem,extracting traditional(hard)ear biometric traits using principal component analysis(PCA)and local binary patterns(LBP),and second,a crowdsourcing-based subsystem,deriving semantic(soft)ear biometric traits.Several feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm’s performance.The obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics,reaching up to 12%for LBP and 5%for PCA descriptors;when fusing all three capacities PCA,LBP,and soft traits using k-nearest neighbors(KNN)classifier.展开更多
文摘A human middle ear consists of an eardrum and three ossicles which are linked by each other, and connect with the eardrum and an inner ear. The inner ear consists of a cochlea and a vestibular system. An abnormality of the human middle ear such as ossicular dislocation may cause conductive hearing loss. The conductive hearing loss is generally treated by surgery using artificial ossicles. The treatments of conductive hearing loss require a better understanding of characteristics and dynamic behaviors of the human middle ear when the sounds transmit from outer inner to inner ear. The purpose of this research is to simulate the dynamic behaviors of a human ear system comprising the middle ear and the cochlea in the inner ear using the finite element method (FEM). Firstly, the eigen-value analysis was performed to obtain the natural frequencies and vibration modes of the total ear system. Secondly, the frequency response analysis was carried out. Thirdly, the time history response analyses were performed using human voices as the external forces. In the time history response analyses, the sounds created as input sound pressures were used. Human voices, for example vowels “I”, “u” and “e” as input sound pressures were created by using the sound pressures downloaded from the opening samples of human voices as wav files in a website. Then it was clarified that the high frequency components of sounds are reduced by the middle ear system.
基金supported and funded by KAU Scientific Endowment,King Abdulaziz University,Jeddah,Saudi Arabia.
文摘Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues.Therefore,biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics.Among different viable biometric modalities,the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems.In recent years,most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results.Nevertheless,such traditional models can be sensitive to several unconstrained environmental factors.As such,some traits may be difficult to extract automatically but can still be semantically perceived as soft biometrics.This research proposes a new group of semantic features to be used as soft ear biometrics,mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each other.Hence,the research study is focused on the fusion of the soft ear biometric traits with traditional(hard)ear biometric features to investigate their validity and efficacy in augmenting human identification performance.The proposed framework has two subsystems:first,a computer vision-based subsystem,extracting traditional(hard)ear biometric traits using principal component analysis(PCA)and local binary patterns(LBP),and second,a crowdsourcing-based subsystem,deriving semantic(soft)ear biometric traits.Several feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm’s performance.The obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics,reaching up to 12%for LBP and 5%for PCA descriptors;when fusing all three capacities PCA,LBP,and soft traits using k-nearest neighbors(KNN)classifier.
