Evaluating spatial correspondence of zones in document recognition systems

During the process of document recognition in a video stream using a mobile device camera, the image quality of the document varies greatly from frame to frame. Sometimes recognition system is required not only to recognize all the specified attributes of the document, but also to select final document image of the best quality. This is necessary, for example, for archiving or providing various services; in some countries it can be required by law. In this case, recognition system needs to assess the quality of frames in the video stream and choose the “best” frame. In this paper we considered the solution to such a problem where the “best” frame means the presence of all specified attributes in a readable form in the document image. The method was set up on a private dataset, and then tested on documents from the open MIDV-2019 dataset. A practically applicable result was obtained for use in recognition systems.

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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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