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access icon free Class-wise two-dimensional PCA method for face recognition

© The Institution of Engineering and Technology
Received 13/05/2016, Accepted 06/09/2016, Revised 22/08/2016, Published 11/09/2016

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.

Inspec keywords: feature extraction; face recognition; video signal processing; principal component analysis; image sensors

Other keywords: biometric identification systems; video files; face image detection; meaningful feature extraction; principal component analysis; class-wise 2D PCA method; PCA-based face recognition techniques

Subjects: Other topics in statistics; Computer vision and image processing techniques; Video signal processing; Image recognition; Other topics in statistics

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