Decision fusion for partially occluded face recognition using common vector approach

KOÇ M.

14th International Scientific and Technical Conference on Computer Science and Information Technologies, CSIT 2019, Lviv, Ukrayna, 17 - 20 Eylül 2019, cilt.1080 AISC, ss.351-360, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 1080 AISC
  • Doi Numarası: 10.1007/978-3-030-33695-0_24
  • Basıldığı Şehir: Lviv
  • Basıldığı Ülke: Ukrayna
  • Sayfa Sayıları: ss.351-360
  • Anahtar Kelimeler: Common vector, Decision fusion, Face recognition, Local binary patterns, Modular, Partial occlusion
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

© Springer Nature Switzerland AG 2020.Partial occlusions in the face image negatively affect the performance of a face recognition system. Modular versions of some methods are used to overcome this problem. Modular Common Vector Approach (MCVA) was successfully applied partial occlusion problem. In this work, we apply some well-known decision fusion methods (product rule, borda count, and majority voting) to the decision stage of MCVA approach to increase the classification performance. A well-known appearance based feature descriptor so called Local Binary Patterns (LBP) is used to extract the facial features. The performance comparisons are conducted on AR face database with several experiments. It is observed that combining the classifier outputs using decision fusion methods increase the classification performance of MCVA.