Human gait recognition based on kernel PCA using projections

Ekinci M., Aykut M.

JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, cilt.22, sa.6, ss.867-876, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 6
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1007/s11390-007-9101-z
  • Dergi Adı: JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.867-876
  • Anahtar Kelimeler: biometrics, gait recognition, gait representation, kernel PCA, pattern recognition
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (PCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.