Featural and configurational processes in the recognition of faces of different familiarity

Previous research suggests that face recognition may involve both configurational and piecemeal (featural) processing. To explore the relationship between these processing modes, we examined the patterns of recognition impairment produced by blurring, inversion, and scrambling, both singly and in various combinations. Two tasks were used: recognition of unfamiliar faces (seen once before) and recognition of highly familiar faces (celebrities). The results provide further support for a configurational - featural distinction. Recognition performance remained well above chance if faces were blurred, scrambled, inverted, or simultaneously inverted and scrambled: each of these manipulations disrupts either configurational or piecemeal processing, leaving the other mode available as a route to recognition. However, blurred/scrambled and blurred/inverted faces were recognised at or near chance levels, presumably because both configurational processing and featural processing were disrupted. Similar patterns of effects were found for both familiar and unfamiliar faces, suggesting that the relationship between configurational and featural processing is qualitatively similar in both cases

Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets. This paper addresses both of those challenges, through an image to video feature-level domain adaptation approach, to learn discriminative video frame representations. The framework utilizes large-scale unlabeled video data to reduce the gap between different domains while transferring discriminative knowledge from large-scale labeled still images. Given a face recognition network that is pretrained in the image domain, the adaptation is achieved by (i) distilling knowledge from the network to a video adaptation network through feature matching, (ii) performing feature restoration through synthetic data augmentation and (iii) learning a domain-invariant feature through a domain adversarial discriminator. We further improve performance through a discriminator-guided feature fusion that boosts high-quality frames while eliminating those degraded by video domain-specific factors. Experiments on the YouTube Faces and IJB-A datasets demonstrate that each module contributes to our feature-level domain adaptation framework and substantially improves video face recognition performance to achieve state-of-the-art accuracy. We demonstrate qualitatively that the network learns to suppress diverse artifacts in videos such as pose, illumination or occlusion without being explicitly trained for them.Comment: accepted for publication at International Conference on Computer Vision (ICCV) 201

The influence of feature-based information in the age processing of unfamiliar faces

The influence of the internal features (eyes, nose, and mouth) in the age processing of unfamiliar faces was examined. Younger and older versions of the faces of six individuals (covering three different age ranges, from infancy to maturity) were used as donor stimuli. For each individual in turn, the effects on age estimates of placing older features in the younger face version (or vice versa) were investigated. Age estimates were heavily influenced by the age of the internal facial features. Experiment 2 replicated these effects with a larger number of faces within a narrower age range (after growth is complete and before major skin changes have occurred). Taken together, these two experiments show that the internal facial features may be influential in conveying age information to the perceiver. However, the mechanisms by which features exert their influence remain difficult to determine: although age estimates might be based on local information from the features themselves, an alternative possibility is that featural changes indirectly influence age estimates by altering the global three-dimensional shape of the head