Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey

Abstract

One important type of biometric authentication is face recognition, a research area of high popularity with a wide spectrum of approaches that have been proposed in the last few decades. The majority of existing approaches are conceived for or evaluated on constrained still images. However, more recently research interests have shifted towards unconstrained "in-the-wild" still images and videos. To some extent, current state-of-the-art systems are able to cope with variability due to pose, illumination, expression, and size, which represent the challenges in unconstrained face recognition. To date, only few attempts have addressed the problem of face recognition in mobile environment, where high degradation is present during both data acquisition and transmission. This book chapter deals with face recognition in mobile and other challenging environments, where both still images and video sequences are examined. We provide an experimental study of one commercial of-the-shelf and four recent open-source face recognition algorithms, including color-based linear discriminant analysis, local Gabor binary pattern histogram sequences, Gabor grid graphs and inter-session variability modeling. Experiments are performed on several freely available challenging still image and video face databases, including one mobile database, always following the evaluation protocols that are attached to the databases. Finally, we supply an easily extensible opensource toolbox to re-run all the experiments, which includes the modeling techniques, the evaluation protocols and metrics used in the experiments, and provides a detailed description on how to re-generate the results.

Deeply vulnerable: a study of the robustness of face recognition to presentation attacks

“…The table also summarizes the hyper-parameters and the scoring method used in each FR system. For the ISV modeling method, we have used the hyper-parameter values recommended by Günther et al [42].…”

Toward Open-Set Face Recognition

“…Thus, we obtain a baseline measurement by computing the cosine similarity between the deep feature vectors of gallery template G g of subject g and probe P . Since each gallery template is composed of three deep feature vectors: G g = (G g 0 , G g 1 , G g 2 ), we apply two strategies: First, we compute three similarities and take the maximum value, which has been shown to provide the best performance in handling several scores [12]:…”

“…Face recognition algorithms have been widely researched over the past decades, resulting in tremendous performance improvements, particularly over the past few years. Even traditional face recognition algorithms, i.e., before the widespread use of deep networks, performed quite well on frontal images under good illumination [12], making them commercially viable for certain applications. For instance, verification scenarios such as automated bor- der control stations [7] allow reasonable control of imaging conditions and subjects usually cooperate with the systemthose that do not are easily spotted by airport security personnel.…”

Heterogeneous Face Recognition Using Inter-Session Variability Modelling

“…In this way future researchers will be able to reproduce exactly the same tests with the same identities in each fold (which is not possible today). This database has a well defined protocol and it is pub- licly available for download 6 . We also organized this protocol in the same way as for CUFS database and it is also freely available for download 7 .…”

“…For simplicity of this analysis the face size and inter-pupil distance were set with constant values. As a reference for those values we used in our experiments the parameters extensively tuned in [6]. This work presents an extensive analysis of face recognition algorithms under different face databases and defined a face size of 80 × 64 pixels and an inter-pupil distance of 33 pixels, after a geometric normalization, as a good trade-off between face size and recognition rate.…”