On projection-based methods for periocular identity verification

The periocular biometric comes into the spotlight recently due to several advantageous characteristics such as easily available and provision of crucial face information. However, many existing works are dedicated to extracting image features using texture based techniques such as local binary pattern (LBP). In view of the simplicity and effectiveness offered, this paper proposes to investigate into projection-based methods for periocular identity verification. Several well established projection-based methods such as principal component analysis, its variants and linear discriminant analysis will be adopted in our performance evaluation based on a subset of FERET face database. Our empirical results show that supervised learning methods significantly outperform those unsupervised learning methods and LBP in terms of equal error rate performance.