Combining hand-crafted and deep-learning features for single sample face recognition

Single Sample Face Recognition (SSFR) is considered one of the most challenging issues in biometrics. This paper suggested a hybrid model to overcome the SSFR problem using two-dimensional face images. Two kinds of features were employed for recognition: the first type was extracted using the robust Multi-block Color Binarized Statistical Image Features (MB-C-BSIF) descriptor, also called hand-crafted features. The other was deep-learning features derived by employing a Convolutional Neural Networks (CNN) model on each face image. This is the first study that combines hand-crafted with deep-learning characteristics for the SSFR issue. We explored whether combining both features can improve recognition performance. Comparative experiments using the AR database indicate that performance improvements can be attained by combining both features, and the fusion of VGG-16-19 with MB-C-BSIF methods achieved the highest accuracy among all the combinations.