Discriminability and reliability indexes: Two new measures to enhance multi-image face recognition

In order to handle complex face image variations in face recognition, multi-image face recognition has been proposed, instead of using a single still-imagebased approach. In many practical scenarios, multiple images can be easily obtained in enrollment and query stages, for example, using video. By accessing these images, a good ”quality” image(s) will be used for recognition using conventional still-image-based recognition algorithms so that the recognition performance can be improved. However, existing methods do not fully utilize all images information. In this paper, two new measurements, namely discriminability index (DI) and reliability index (RI), are proposed to evaluate the enrolled and query images respectively. By considering the distribution of enrolled images from individuals, the discriminability index of each image is calculated and a weight is assigned. For testing images, a reliability index is calculated based on matching quality between the testing image and enrolled images. If the reliability index of a testing image is small, the testing image will be discarded as it may degrade the recognition performance. To evaluate and demonstrate the use of DI and RI, we adopt the recognition algorithm using combining classifiers with eigenface representations in input and kernel spaces. CMU-PIE , YaleB and FRGC databases are used for experiments. Experimental results show that the recognition performance, with three popular combination rules, can be increased by more than 10% on average with the use of DI and RI.