Detail publikace

Gender Recognition Using PCA and DCT of Face Images

Originální název

Gender Recognition Using PCA and DCT of Face Images

Anglický název

Gender Recognition Using PCA and DCT of Face Images

Jazyk

en

Originální abstrakt

In this paper we propose a gender recognition algorithm of face images. We have used PCA and DCT for dimensionality reduction. The algorithm is based on a genetic algorithm to improve the selection of training set of images for the PCA algorithm. Genetic algorithm helps to select the images, which best represent each gender, from the image database. We have evaluated a nearest neighbor classifier as well as a neural network. Experimental results show a correct identification rate of 85,9%.

Anglický abstrakt

In this paper we propose a gender recognition algorithm of face images. We have used PCA and DCT for dimensionality reduction. The algorithm is based on a genetic algorithm to improve the selection of training set of images for the PCA algorithm. Genetic algorithm helps to select the images, which best represent each gender, from the image database. We have evaluated a nearest neighbor classifier as well as a neural network. Experimental results show a correct identification rate of 85,9%.

BibTex


@article{BUT36035,
  author="Ondřej {Šmirg} and Marcos {Faúndez Zanuy} and Marco {Grassi} and Jiří {Mekyska} and Jan {Mikulka}",
  title="Gender Recognition Using PCA and DCT of Face Images",
  annote="In this paper we propose a gender recognition algorithm of face images. We have used PCA and DCT for dimensionality reduction. The algorithm is based on a genetic algorithm to improve the selection of training set of images for the PCA algorithm. Genetic algorithm helps to select the images, which best represent each gender, from the image database. We have evaluated a nearest neighbor classifier as well as a neural network. Experimental results show a correct identification rate of 85,9%.",
  address="Springer-Verlag",
  chapter="36035",
  institution="Springer-Verlag",
  number="6",
  volume="6692",
  year="2011",
  month="june",
  pages="220--227",
  publisher="Springer-Verlag",
  type="journal article"
}