Occluded Face Recognition Using Correntropy-based Nonnegative Matrix Factorization

Ensari T., Chorowski J., Zurada J. M.

11th IEEE International Conference on Machine Learning and Applications (ICMLA), Florida, Amerika Birleşik Devletleri, 12 - 15 Aralık 2012, ss.606-609 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/icmla.2012.112
  • Basıldığı Şehir: Florida
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.606-609
  • İstanbul Üniversitesi Adresli: Evet

Özet

Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a well approximated product using an objective function. In this paper we propose to maximize the correntropy similarity measure as an objective function for NMF. Correntropy has been recently defined as a nonlinear similarity measure using an entropy-based criterion. After the minimization process of the correntropy function, we use it to recognize occluded face data set and compare its recognition performance with the standard NMF and Principal Component Analysis (PCA). The experimental results are illustrated with ORL face data set. The results show that our correntropy-based NMF (NMF-Corr) has better recognition rate compared with PCA and NMF.