Multimodal Human Recognition Systems

Signal Processing and Communications - Multi-Sensor Image Fusion and Its Applications ◽

10.1201/9781420026986.ch9 ◽

2005 ◽

pp. 289-301 ◽

Cited By ~ 2

Author(s):

Anil Jain ◽

Arun Ross

Keyword(s):

Human Recognition ◽

Recognition Systems

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Low Power Tiny Binary Neural Network with improved accuracy in Human Recognition Systems

2020 23rd Euromicro Conference on Digital System Design (DSD) ◽

10.1109/dsd51259.2020.00057 ◽

2020 ◽

Author(s):

Antonio De Vita ◽

Danilo Pau ◽

Luigi Di Benedetto ◽

Alfredo Rubino ◽

Frederic Petrot ◽

...

Keyword(s):

Neural Network ◽

Low Power ◽

Human Recognition ◽

Binary Neural Network ◽

Recognition Systems ◽

Improved Accuracy

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Human Ear Pattern Recognition System

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.35 ◽

2017 ◽

Vol 7 (8) ◽

pp. 117 ◽

Cited By ~ 1

Author(s):

V. Jagan Naveen ◽

K. Krishna Kishore ◽

P. Rajesh Kumar

Keyword(s):

Pattern Recognition ◽

Expectation Maximization Algorithm ◽

Recognition System ◽

Modern World ◽

Person Identification ◽

Biometric Systems ◽

Different Types ◽

Recognition Systems ◽

Human Ear ◽

Pca Algorithm

In the modern world, human recognition systems play an important role to improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm. Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

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Facial recognition technologies and the new physiognomic era

Kritische Lebenskunst - psychosozial ◽

10.30820/0171-3434-2020-2-45 ◽

2020 ◽

Vol 43 (2) ◽

pp. 45-56

Author(s):

Abigail Nieves Delgado

Keyword(s):

Facial Recognition ◽

Personal Identification ◽

Digital Photographs ◽

Different Types ◽

Recognition Systems ◽

The Way

The current overproduction of images of faces in digital photographs and videos, and the widespread use of facial recognition technologies have important effects on the way we understand ourselves and others. This is because facial recognition technologies create new circulation pathways of images that transform portraits and photographs into material for potential personal identification. In other words, different types of images of faces become available to the scrutiny of facial recognition technologies. In these new circulation pathways, images are continually shared between many different actors who use (or abuse) them for different purposes. Besides this distribution of images, the categorization practices involved in the development and use of facial recognition systems reinvigorate physiognomic assumptions and judgments (e.g., about beauty, race, dangerousness). They constitute the framework through which faces are interpreted. This paper shows that, because of this procedure, facial recognition technologies introduce new and far-reaching »facialization« processes, which reiterate old discriminatory practices.

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