Face Recognition with VG-RAM Weightless Neural Networks

Authors:
Alberto F. Souza;Claudine Badue;Felipe Pedroni;Elias Oliveira;Stiven Schwanz Dias;Hallysson Oliveira;Soterio Ferreira Souza
Affiliations:
Departamento de Informática, ,;Departamento de Informática, ,;Departamento de Informática, ,;Departamento de Ciência da Informação, Universidade Federal do Espírito Santo, Vitória-ES, Brazil 29075-910;Departamento de Informática, ,;Departamento de Informática, ,;Departamento de Informática, ,
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

Citing 6
Cited 2

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Face Recognition Using Face-ARG Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience

VG-RAM WNN approach to monocular depth perception

ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Fast learning and predicting of stock returns with virtual generalized random access memory weightless neural networks

Concurrency and Computation: Practice & Experience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Virtual Generalizing Random Access Memory Weightless Neural Networks (Vg-ram wnn) are effective machine learning tools that offer simple implementation and fast training and test. We examined the performance of Vg-ram wnnon face recognition using a well known face database--the AR Face Database. We evaluated two Vg-ram wnnarchitectures configured with different numbers of neurons and synapses per neuron. Our experimental results show that, even when training with a single picture per person, Vg-ram wnnare robust to various facial expressions, occlusions and illumination conditions, showing better performance than many well known face recognition techniques.