Neural network recognition of human face images stored in the database

Badal, Dushan Z.

doi:10.1109/pccc.1993.344529

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…In addition, different positions of the face images have a great impact on the accuracy of face recognition. In addition, the accuracy of face recognition can be improved by 4%~8% even if the training set is enlarged by only a few additional face views (Badal, 1993).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A virtual keyboard implementation based on finger recognition

Zhang

Yan

Narayanan

2017

2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)

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A keyboard requires a great deal of resources and is restricted by its physical features. Additionally, discarded keyboards also inevitably contribute to environmental pollution. Consequently, the touch screen is designed to replace the physical keyboard and thus reduce these flaws. However, the internal digital keyboard on the touch screen takes up a substantial amount of space, which causes some content to be covered. Moreover, the touch screen can be dirtied by fingerprints and become worn over time by human fingernails through frequent use. Hence, it is necessary to develop a new type of environment-friendly virtual keyboard with fewer flaws. In this thesis, the author develops a novel type of virtual keyboard that allows users to use fingers to type on a piece of paper at any fixed plane. Finger recognition is based on human skin tone and then the BWMORPH algorithm is utilized to recognize the user's fingertip. If the user's fingertip has remained on a key for a long time, the program will regard this key as an input. The experiments in this thesis adopt five kinds of customized paper keyboards on a wall to demonstrate the usability of the proposed virtual keyboard. Typing without touching the keyboard is fulfilled to ignore obstrutions covering the paper keyboard. The experiment results indicate that the overall recognition rate of the proposed virtual keyboard is 94.62%. The proposed virtual keyboard can be put to use for a smartphone in the future. Furthermore, as a blueprint, it can be applied to computers after it is improved to allow ten-finger recognition. Moreover, machine learning can be potentially embedded into our virtual keyboard so as to greatly improve its finger recognition performance.

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Section: Artificial Neural Networkmentioning

confidence: 99%

A virtual keyboard implementation based on finger recognition

Zhang

Yan

Narayanan

2017

2017 International Conference on Image and Vision Computing New Zealand (IVCNZ)

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“…Este tipo de red lleva a cabo internamente el Análisis de Componentes Principales (como se indicará en el Capítulo 6). Existen numerosas publicaciones sobre reconocimiento facial empleando clasificadores de redes neuronales, entre ellas se encuentran [Bada93][Nara94][Lawr97]. A continuación se indican algunos trabajos relevantes dedicados a la clasificación de los individuos de acuerdo a su sexo.…”

Section: Técnicas Conexionistasunclassified

Reconocimiento facial automático mediante técnicas de visión tridimensional

Díaz¹

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This Ph. D. Thesis presents new approaches to Automatic Face Recognition using 3D Vision techniques. 3D Vision is the part of Computer Vision that uses three-dimensional properties of a real scene (obtained from its digitizatlon) to achieve its interpretation. This Tfiesis focuses on tlie recognition of human faces using 3D information.New representations of 3D shapes are proposed in this Thesis in order to implement systems that are based on 3D holistic information of the face. Principal Components Analysis (PCA) and Support Vector Machines (SVM), two widely used techniques for 2D images, have been successfiílly used in combination with these new 3D representations to implement Face Recognition Systems.In order to efficiently use PCA and SVM tlie Thesis describes the design of a stage that corrects the position of 3D images and that obtains a volumetric regular representation using voxels. This new representation, obtained from facial meshes, is an alternative to the depth maps that have been employed until now in this kind of holistic 3D face recognition systems.

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An integrity constraint for database systems containing embedded neural networks

Millns

Eaglestone

Proceedings Ninth International Workshop on Database and Expert Systems Applications (Cat. No.98EX130)

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Neural network recognition of human face images stored in the database

Cited by 3 publications

References 27 publications

A virtual keyboard implementation based on finger recognition

A virtual keyboard implementation based on finger recognition

Reconocimiento facial automático mediante técnicas de visión tridimensional

An integrity constraint for database systems containing embedded neural networks

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