Hybrid component-based face recognition.

Abstract

Facial recognition (FR) is the trusted biometric method for authentication. Compared to other biometrics such as signature; which can be compromised, facial recognition is non-intrusive and it can be apprehended at a distance in a concealed manner. It has a significant role in conveying the identity of a person in social interaction and its performance largely depends on a variety of factors such as illumination, facial pose, expression, age span, hair, facial wear, and motion. In the light of these considerations this dissertation proposes a hybrid component-based approach that seeks to utilise any successfully detected components. This research proposes a facial recognition technique to recognize faces at component level. It employs the texture descriptors Grey-Level Co-occurrence (GLCM), Gabor Filters, Speeded-Up Robust Features (SURF) and Scale Invariant Feature Transforms (SIFT), and the shape descriptor Zernike Moments. The advantage of using the texture attributes is their simplicity. However, they cannot completely characterise the whole face recognition, hence the Zernike Moments descriptor was used to compute the shape properties of the selected facial components. These descriptors are effective facial components feature representations and are robust to illumination and pose changes. Experiments were performed on four different state of the art facial databases, the FERET, FEI, SCface and CMU and Error-Correcting Output Code (ECOC) was used for classification. The results show that component-based facial recognition is more effective than whole face and the proposed methods achieve 98.75% of recognition accuracy rate. This approach performs well compared to other componentbased facial recognition approaches.