Face Detection and Classification Using Eigenfaces and Principal Component Analysis: Preliminary Results. Mejia-Campos, R., Nejer-Haro, D., Recalde-Avincho, S., Rosero-Montalvo, P., & Peluffo-Ordonez, D. In 2017 International Conference on Information Systems and Computer Science (INCISCOS), pages 309-315, 11, 2017. IEEE. ![Face Detection and Classification Using Eigenfaces and Principal Component Analysis: Preliminary Results [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex This work is a Scientific Track paper corresponding to the area of Intelligent Systems. This paper presents a facial recognition approach based on the Eigenfaces method as well as Principal Component Analysis (PCA) as algorithm of processing and cleaning images, respectively. The classification was performed by using the Euclidean distance between the facial characters stored in a database and new images captured in an interface with similarly coded developed in MatLab. As main results, we obtained: (i) 68.9% of classification accuracy when using different components of stored faces, (ii) 91.43% of classification performance when storing 3 components for each face and evaluating more users for training model in seven controlled experiments.
@inproceedings{
title = {Face Detection and Classification Using Eigenfaces and Principal Component Analysis: Preliminary Results},
type = {inproceedings},
year = {2017},
keywords = {Eigenfaces,PCA,face recognition},
pages = {309-315},
websites = {http://ieeexplore.ieee.org/document/8328124/},
month = {11},
publisher = {IEEE},
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abstract = {This work is a Scientific Track paper corresponding to the area of Intelligent Systems. This paper presents a facial recognition approach based on the Eigenfaces method as well as Principal Component Analysis (PCA) as algorithm of processing and cleaning images, respectively. The classification was performed by using the Euclidean distance between the facial characters stored in a database and new images captured in an interface with similarly coded developed in MatLab. As main results, we obtained: (i) 68.9% of classification accuracy when using different components of stored faces, (ii) 91.43% of classification performance when storing 3 components for each face and evaluating more users for training model in seven controlled experiments.},
bibtype = {inproceedings},
author = {Mejia-Campos, Richard and Nejer-Haro, Diego and Recalde-Avincho, Santiago and Rosero-Montalvo, Paul and Peluffo-Ordonez, Diego},
doi = {10.1109/INCISCOS.2017.59},
booktitle = {2017 International Conference on Information Systems and Computer Science (INCISCOS)}
}
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