Feature scalability for a low complexity face recognition with unconstrained spatial resolution

Abstract

Automatic face recognition (FR) based applications in low computing power constrained systems, such as mobile and smart camera, have become particularly interesting topic in recent years. In this context, we present computationally efficient FR framework underpinning the so-called feature scalability algorithm. The proposed framework aims at implementing robust FR systems under low-computing power restriction and varying face resolution. Key beneficial property of our proposed FR framework based on feature scalability is to require low computational complexity without sacrificing a level of FR performance. To do this, using feature scalability algorithm enables to directly estimate the features (from pre-enrolled gallery images) that are well matched with the feature of an input probe image with different resolution (generally lower resolution) without any complex process. In addition, our method is helpful for relieving storage shortage problem as it does not require a large amount of training and gallery images with different face resolutions. Results show that our proposed feature scalability algorithm can be seamlessly embedded into state-of-the-art feature extraction methods extensively used for FR by achieving impressive recognition performance. Also, according to the results on computational complexity measurement, the proposed method is proven to be useful for substantially saving FR operation time.