Gender classification via gradientfaces

Loǧoǧlu K. B., Saracoǧlu A., Esen E., ALATAN A. A.

25th International Symposium on Computer and Information Sciences, ISCIS 2010, London, İngiltere, 22 - 24 Eylül 2010, cilt.62 LNEE, ss.245-251 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 62 LNEE
  • Doi Numarası: 10.1007/978-90-481-9794-1_48
  • Basıldığı Şehir: London
  • Basıldığı Ülke: İngiltere
  • Sayfa Sayıları: ss.245-251
  • Anahtar Kelimeler: Gender Classification, Gradientface, Support Vector Machine
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this paper illumination invariant, pose and facial expression tolerant gender classification method is proposed. A recently introduced feature extraction method, namely Gradientfaces, is utilized together with Support Vector Machine (SVM) as a classifier. Image regions obtained from cascaded Adaboost based face detector is used at the feature extraction step and faster classification is achieved by using only 20-by-20 pixel region during feature extraction. For performance evaluation, two well-known face databases, FERET and Yale B are tested and the algorithm is compared against a pixelbased algorithm on these datasets. The results indicate that Gradientfaces significantly outperform the pixel-based methods under severe illumination, pose and facial expression variances. © 2011 Springer Science+Business Media B.V.