An approach towards ear feature extraction for human identification

Saranya, M.; Cyril, G. L. Infant; Santhosh, R.

doi:10.1109/iceeot.2016.7755636

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…A. Abaza [9] proposed an ear detection device the usage of quicker CNN in which Alex internet version is used for segmenting the layers of ear and checking out accuracy is round 98%. M. Saranya [10] proposed a totally computerized ear reputation device in which the function extraction is carried out primarily based totally on the "Adaptive blocks" method and performance is improved. Thirimachos Bourlai [11] proposed a CNN primarily based totally ear detection technique via way of means of thinking about manyimage degradations factors, in which the versions blanketed are brightness, contrast, additive noise etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Ear Biometrics and Face Mask Detection Using Haar Features

A¹,

T²

2022

IJERECE

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The world depends on systems to provide secure environments and services to the people. Biometrics authentication (or realistic authentication) is employed in applied science as a style of identification and access control. Biometric System for authentic identification of a private. My motto is to make a biometric system using the ear as the main object. It also has no changes as expression change The human ear are neither affected by expressions like faces are nor do need closer touching like finger-prints do, which is more useful in the situation where the protection mechanism is needed as like in the pandemic Covid19 situation. Ear biometrics appears to be an accurate approach to an ever increasing demand for security in the common spaces. Then the robust feature extraction method is often accustomed determine personality of some individuals, as an example terrorist at the airport terminals. Ear as biometrics is often used in multimodal systems to improve the performance of some other known biometrics. In this paper, a novel algorithm was proposed to do face mask detection and ear recognition using deep convolutional neural network and provide a visualization of the learned network. Also a temperature sensor is included. Only after the checking of facemask the subject is allowed to ear biometrics for more security reason.

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Section: Literature Reviewmentioning

confidence: 99%

Ear Biometrics and Face Mask Detection Using Haar Features

A¹,

T²

2022

IJERECE

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“…Due to this, the classification of human accessories in semantic video analysis tasks has attracted a great interest in recent years within the research community [ 3 ], since it allows for semantically describing people for different applications, such as security and biometrics [ 4 , 5 ], surveillance [ 6 ], human computer interaction [ 7 ], or oriented marketing [ 8 ]. Recently, there has been an increasing number of approaches proposed in the scientific literature, which are able to extract human attributes such as gender, ethnicity, age, hairstyle or clothing, focused on in such applications.…”

Section: Introductionmentioning

confidence: 99%

Headgear Accessories Classification Using an Overhead Depth Sensor

Luna

Macías-Guarasa

Losada

et al. 2017

Sensors

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In this paper, we address the generation of semantic labels describing the headgear accessories carried out by people in a scene under surveillance, only using depth information obtained from a Time-of-Flight (ToF) camera placed in an overhead position. We propose a new method for headgear accessories classification based on the design of a robust processing strategy that includes the estimation of a meaningful feature vector that provides the relevant information about the people’s head and shoulder areas. This paper includes a detailed description of the proposed algorithmic approach, and the results obtained in tests with persons with and without headgear accessories, and with different types of hats and caps. In order to evaluate the proposal, a wide experimental validation has been carried out on a fully labeled database (that has been made available to the scientific community), including a broad variety of people and headgear accessories. For the validation, three different levels of detail have been defined, considering a different number of classes: the first level only includes two classes (hat/cap, and no hat/cap), the second one considers three classes (hat, cap and no hat/cap), and the last one includes the full class set with the five classes (no hat/cap, cap, small size hat, medium size hat, and large size hat). The achieved performance is satisfactory in every case: the average classification rates for the first level reaches 95.25%, for the second one is 92.34%, and for the full class set equals 84.60%. In addition, the online stage processing time is 5.75 ms per frame in a standard PC, thus allowing for real-time operation.

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