Class-wise two-dimensional PCA method for face recognition

GÜZEL TURHAN C., BİLGE H. Ş.

IET COMPUTER VISION, cilt.11, sa.4, ss.286-300, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 4
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1049/iet-cvi.2016.0135
  • Dergi Adı: IET COMPUTER VISION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.286-300
  • Anahtar Kelimeler: face recognition, feature extraction, principal component analysis, video signal processing, image sensors, class-wise 2D PCA method, PCA-based face recognition techniques, biometric identification systems, face image detection, meaningful feature extraction, principal component analysis, video files, PRINCIPAL COMPONENT ANALYSIS, BIDIRECTIONAL PCA, REPRESENTATION, ROBUST, 2DPCA, EIGENFACES, L1-NORM
  • Gazi Üniversitesi Adresli: Evet

Özet

Interests in biometric identification systems have led to many face recognition task-oriented studies. These studies often address the detection of face images taken from a camera and the recognition of faces via extracted meaningful features. To meet the requirement of defining data with fewer features, principal component analysis (PCA)-based techniques are widely used due to their efficiency and simplicity. There is a remarkable interest in the used efficiency of PCA by extending this traditional technique with various aspects. From this viewpoint, this study is specifically focused on the PCA-based face recognition techniques. By enhancing the methods in the reviewed studies, a novel class-wise two-dimensional PCA-based face recognition algorithm is presented in this study. Unlike the traditional method, this method generates more than one subspace considering within-class scattering. A system based on the presented approach can successively detect and recognise faces in not only images but also in video files. In addition, analyses were conducted to evaluate the efficiency of the proposed algorithm and its extension comparing with other addressed PCA-based methods. On the basis of the experimental results, it is clear to say that the presented approach and its extension are superior to the compared PCA-based algorithms.