Carina A. Hahn

Creating Body Shapes From Verbal Descriptions by Linking Similarity Spaces

Brief verbal descriptions of people’s bodies (e.g., “curvy,” “long-legged”) can elicit vivid mental images. The ease with which these mental images are created belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and showed that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body-shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2,094 bodies. This relationship allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape that captures perceptually salient global and local body features.

Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms

Significance This study measures face identification accuracy for an international group of professional forensic facial examiners working under circumstances that apply in real world casework. Examiners and other human face “specialists,” including forensically trained facial reviewers and untrained superrecognizers, were more accurate than the control groups on a challenging test of face identification. Therefore, specialists are the best available human solution to the problem of face identification. We present data comparing state-of-the-art face recognition technology with the best human face identifiers. The best machine performed in the range of the best humans: professional facial examiners. However, optimal face identification was achieved only when humans and machines worked in collaboration. Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.

Recognizing approaching walkers: Neural decoding of person familiarity in cortical areas responsive to faces, bodies, and biological motion

Abstract In natural viewing environments, we recognize other people as they move through the world. Behavioral studies indicate that the face, body, and gait all contribute to recognition. We examined the neural basis of person recognition using a decoding approach aimed at discriminating the patterns of neural activity elicited in response to seeing visually familiar versus unfamiliar people in motion. Participants learned 30 identities by viewing multiple videos of the people in action. Recognition was tested inside a functional magnetic resonance imaging (fMRI) scanner using 8‐s videos of 60 people (30 learned and 30 novel) approaching from a distance (˜13 m). Full brain images were taken while participants watched the approach. These images captured neural activity at four time points (TRs) corresponding to progressively closer views of the walker. We used pattern classification techniques to examine familiarity decoding in lateralized ROIs and the combination of left and right (bilateral) regions. Results showed accurate decoding of familiarity at the farthest distance in the bilateral posterior superior temporal sulcus (bpSTS). At a closer distance, familiarity was decoded in the bilateral extrastriate body area (bEBA) and left fusiform body area (lFBA). The most robust decoding was found in the time window during which the average behavioral recognition decision was made – and when the face came into clearer view. Multiple regions, including the right occipital face area (rOFA), bOFA, bFBA, bpSTS, and broadly distributed face‐ and body‐selective voxels in the ventral temporal cortex decoded walker familiarity in this time window. At the closest distance, the lFBA decoded familiarity. These results reveal a broad system of ventral and dorsal visual areas that support person recognition from face, body, and gait. Although the face has been the focus of most person recognition studies, these findings remind us of the evolutionary advantage of being able to differentiate the people we know from strangers at a safe distance. HighlightsNeurally decoded familiarity of walkers in ventral and dorsal visual stream ROIs.Neural decoding occurred when walkers were most distant in the dorsal stream pSTS.Face‐ and body‐selective regions decoded familiarity: bEBA, lFBA, rOFA, bOFA.Interactions among collections of face‐ and body‐selective voxels for recognition.Widespread neural network signals the familiarity of a person in motion.