Investigating intra-individual variability of face scanning in autistic children

We investigated the intra-individual variability of face scanning in autistic children to represent a new avenue for understanding abnormal face scanning in autism spectrum condition. Across four studies, we used eye-tracking techniques to systematically examine the variability of face scanning patterns in autistic children when performing different tasks and scanning different types of faces. Autistic and non-autistic children were asked to complete a face judgment task (Study 1, age range: 4.9–7.2 years), a face recognition task (Study 2, age range: 4.7–7.6 years), a facial expression recognition task (Study 3, age range: 4.3–7.4 years), and a dynamic facial expression free viewing task (Study 4, age range: 2.5–5.6 years). In addition, we conducted Study 5 using houses as stimuli to test the specificity of the results to faces (age range: 4.9–7.2 years). We found that scan pattern similarity between different face presentations was lower in autistic children than non-autistic children, which was robust to variations in experimental methods. Furthermore, the decreased scan pattern similarity in autism spectrum condition was evident in both viewing faces and houses. These results suggest that the scanning patterns of autistic children are noisier and variable. It might represent a new avenue for the understanding of core symptoms in autism spectrum condition. Lay abstract Atypical face scanning is suggested to be related to social interactions and communicative deficits in autistic children. We systematically examined whether autistic and non-autistic children used consistent scanning patterns when performing different tasks and scanning different types of faces. We found that autistic children scanned faces more variably than non-autistic children: While non-autistic children used more consistent scanning patterns, autistic children’s scanning patterns changed frequently when watching different faces. Autistic children’s variable face scanning patterns might delay and impair face processing, resulting in a social interaction deficit. What’s more, variable scanning patterns may create an unstable and unpredictable perception of the environment for autistic children. Developing in such an unstable environment might motivate autistic children to retract from the environment, avoid social interaction, and focus instead on the performance of repetitive behavior. Therefore, studying face scanning variability might represent a new avenue for understanding core symptoms in autistic people.

Discriminative Dictionary Learning for Autism Spectrum Disorder Identification

Autism Spectrum Disorder (ASD) is a group of lifelong neurodevelopmental disorders with complicated causes. A key symptom of ASD patients is their impaired interpersonal communication ability. Recent study shows that face scanning patterns of individuals with ASD are often different from those of typical developing (TD) ones. Such abnormality motivates us to study the feasibility of identifying ASD children based on their face scanning patterns with machine learning methods. In this paper, we consider using the bag-of-words (BoW) model to encode the face scanning patterns, and propose a novel dictionary learning method based on dual mode seeking for better BoW representation. Unlike k-means which is broadly used in conventional BoW models to learn dictionaries, the proposed method captures discriminative information by finding atoms which maximizes both the purity and coverage of belonging samples within one class. Compared to the rich literature of ASD studies from psychology and neural science, our work marks one of the relatively few attempts to directly identify high-functioning ASD children with machine learning methods. Experiments demonstrate the superior performance of our method with considerable gain over several baselines. Although the proposed work is yet too preliminary to directly replace existing autism diagnostic observation schedules in the clinical practice, it shed light on future applications of machine learning methods in early screening of ASD.

MAC Based Security Integration using Face Recognition in Cloud Environment

The major challenges, which come across face recognition system, are to find the age and gender in 2D/3D image of the person specifically in cloud environment. This paperis centered on face detection with MAC (Media Access Control) and biometric technology. Face scanning along with machine’s MAC address and biometric technologies has been shown to improve security controls. Face recognition can be used to search and label users and their assigned machines for sensitive purposes. Following that, it was stored in a specific database with their unique ID. In addition, the verification process has begun by comparing the models in the database. Face scanning along with speech and biometric technologies is used to improve security controls. Face recognition system may also be set up in high security machines to improve protection by allowing only registered individuals or others users. Related strategies for determining the age and gender and 2D/3D image from a specific picture are explored, as well as several modern methods for preserving protection. In this paper, the full model is explored independently with security implemented in cloud environment. The proposed model of the paper provides the integrated security features using MAC address of machine and face recognition of the machine user.