Rapid neural categorization of angry and fearful faces is specifically impaired in boys with autism spectrum disorder.

BACKGROUND: Difficulties with facial expression processing may be associated with the characteristic social impairments in individuals with autism spectrum disorder (ASD). Emotional face processing in ASD has been investigated in an abundance of behavioral and EEG studies, yielding, however, mixed and inconsistent results. METHODS: We combined fast periodic visual stimulation (FPVS) with EEG to assess the neural sensitivity to implicitly detect briefly presented facial expressions among a stream of neutral faces, in 23 boys with ASD and 23 matched typically developing (TD) boys. Neutral faces with different identities were presented at 6 Hz, periodically interleaved with an expressive face (angry, fearful, happy, sad in separate sequences) every fifth image (i.e., 1.2 Hz oddball frequency). These distinguishable frequency tags for neutral and expressive stimuli allowed direct and objective quantification of the expression-categorization responses, needing only four sequences of 60 s of recording per condition. RESULTS: Both groups show equal neural synchronization to the general face stimulation and similar neural responses to happy and sad faces. However, the ASD group displays significantly reduced responses to angry and fearful faces, compared to TD boys. At the individual subject level, these neural responses allow to predict membership of the ASD group with an accuracy of 87%. Whereas TD participants show a significantly lower sensitivity to sad faces than to the other expressions, ASD participants show an equally low sensitivity to all the expressions. CONCLUSIONS: Our results indicate an emotion-specific processing deficit, instead of a general emotion-processing problem: Boys with ASD are less sensitive than TD boys to rapidly and implicitly detect angry and fearful faces. The implicit, fast, and straightforward nature of FPVS-EEG opens new perspectives for clinical diagnosis.

Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach.

Face recognition is a widely accepted biometric identifier, as the face contains a lot of information about the identity of a person. The goal of this study is to match the 3D face of an individual to a set of demographic properties (sex, age, BMI, and genomic background) that are extracted from unidentified genetic material. We introduce a triplet loss metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. The metric learner is trained for multiple facial segments to allow a global-to-local part-based analysis of the face. To learn directly from 3D mesh data, spiral convolutions are used along with a novel mesh-sampling scheme, which retains uniformly sampled points at different resolutions. The capacity of the model for establishing identity from facial shape against a list of probe demographics is evaluated by enrolling the embeddings for all properties into a support vector machine classifier or regressor and then combining them using a naive Bayes score fuser. Results obtained by a 10-fold cross-validation for biometric verification and identification show that part-based learning significantly improves the systems performance for both encoding with our geometric metric learner or with principal component analysis.

Investigating automatic emotion processing in boys with autism via eye tracking and facial mimicry recordings.

Difficulties in automatic emotion processing in individuals with autism spectrum disorder (ASD) might remain concealed in behavioral studies due to compensatory strategies. To gain more insight in the mechanisms underlying facial emotion recognition, we recorded eye tracking and facial mimicry data of 20 school-aged boys with ASD and 20 matched typically developing controls while performing an explicit emotion recognition task. Proportional looking times to specific face regions (eyes, nose, and mouth) and face exploration dynamics were analyzed. In addition, facial mimicry was assessed. Boys with ASD and controls were equally capable to recognize expressions and did not differ in proportional looking times, and number and duration of fixations. Yet, specific facial expressions elicited particular gaze patterns, especially within the control group. Both groups showed similar face scanning dynamics, although boys with ASD demonstrated smaller saccadic amplitudes. Regarding the facial mimicry, we found no emotion specific facial responses and no group differences in the responses to the displayed facial expressions. Our results indicate that boys with and without ASD employ similar eye gaze strategies to recognize facial expressions. Smaller saccadic amplitudes in boys with ASD might indicate a less exploratory face processing strategy. Yet, this slightly more persistent visual scanning behavior in boys with ASD does not imply less efficient emotion information processing, given the similar behavioral performance. Results on the facial mimicry data indicate similar facial responses to emotional faces in boys with and without ASD. LAY SUMMARY: We investigated (i) whether boys with and without autism apply different face exploration strategies when recognizing facial expressions and (ii) whether they mimic the displayed facial expression to a similar extent. We found that boys with and without ASD recognize facial expressions equally well, and that both groups show similar facial reactions to the displayed facial emotions. Yet, boys with ASD visually explored the faces slightly less than the boys without ASD.