Low-quality facial biometric verification via dictionary-based random pooling

• Investigate face recognition in the realistic low-resolution surveillance world.
• Apply sparse-coding based high-quality dictionary into low-quality face recognition.
• Convert LR face recognition into a matching problem of HR visual words.
• Exploit random pooling for a better matching accuracy.
• Validate the proposed scheme in both theoretical and experimental worlds.

In the past decade, visual surveillance has emerged as an effective tool in public security applications. Due to the technical limitations of both surveillance cameras and transmission speed, videos collected from surveillance sites are usually of low resolution. Especially, facial images at a distance in surveillance videos are usually at very low quality, making it difficult to carry out automated facial biometric verification. To handle with this challenge, in this work, we introduce dictionary based techniques to cope with low quality facial images, and propose a random pooling scheme to enhance the accuracy of facial biometric verification. In the proposed scheme, a dictionary is first learned from paired low-resolution and high-resolution facial images, and the input low-resolution query face can then be modelled by a set of high-resolution visual words via a dictionary lookup. A random pooling strategy is then applied to select subsets of visual words, and kernel Fisher׳s linear discriminant analysis (k-LDA) is introduced to find the discriminant metrics. The final decision is based on the average over different pooling results. The experiment on three publically available face datasets validated that our proposed scheme can robustly cope with the challenges from low quality facial images, and attained an improved accuracy over all datasets, making our method a promising candidate for facial biometric based security applications.