Identification of Autism in Children Using Static Facial Features and Deep Neural Networks

Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents’ daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children’s face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level.

Eye Size Affects Cuteness in Different Facial Expressions and Ages

Researchers have suggested that infants exhibiting baby schema are considered cute. These similar studies have mainly focused on changes in overall baby schema facial features. However, whether a change in only eye size affects the perception of cuteness across different facial expressions and ages has not been explicitly evaluated until now. In the present study, a paired comparison method and 7-point scale were used to investigate the effects of eye size on perceived cuteness across facial expressions (positive, neutral, and negative) and ages (adults and infants). The results show that stimuli with large eyes were perceived to be cuter than both unmanipulated eyes and small eyes across all facial expressions and age groups. This suggests not only that the effect of baby schema on cuteness is based on changes in a set of features but also that eye size as an individual feature can affect the perception of cuteness.

Esquirol-Séguin-Down Syndrome Associated with Atrial Septal Defect plus Tricuspid Regurgitation

Background: Esquirol-Séguin-Down syndrome (Trisomy 21) was first described by Jean-Etienne Dominique Esquirol in 1838 and later by Edouard Séguin in 1846. Thereafter, in 1862, John Langdon Down, a British physician emphasized that the syndrome is a distinct form of mental retardation. Congenital cardiac defects are observed in more than one third of the patients with Esquirol-Séguin-Down syndrome, and in approximately 80% these cardiac defects are atrioventricular septal defect or ventricular septal defect with the former being more common. The association of Esquirol-Séguin-Down syndrome with atrial septal defect plus tricuspid regurgitation has been rarely reported. The aim of this paper is to report the rare association of Esquirol-Séguin-Down syndrome with atrial septal defect plus tricuspid regurgitation. Patients and methods: Two and half years old boy with Esquirol-Séguin-Down syndrome, developmental delay and abnormal echocardiography was studied, and the recent relevant literatures were reviewed. Results: Dysmorphic facial features included hypertelorism, oblique palpebral fissures, epicanthic folds, depressed nasal bridge and low set ears. Echocardiography showed atrial septal defect with tricuspid regurgitation. Conclusion: This paper reports the first case of Esquirol-Séguin-Down syndrome in Iraq associated with atrial septal defect plus tricuspid regurgitation.

Face Detection With Face Mask for COVID-19 Pandemic Using Neural Networks

The COVID-19 virus can be spread through contact and contaminated surfaces; therefore, typical biometric systems like password and fingerprint are unsafe. Face recognition solutions are safer without any need of touching any device. During the COVID-19 situation as all of the people are advised to wear masks on their faces, the existing face detection technique is not able to identify the person with face occlusion. The fraudsters and thieves take advantage of this scenario and misuse the face mask, favoring them to be able to steal and commit various crimes without being identified. Face recognition methods fail to detect or recognize the face as half of the face is masked and the features are suppressed. Face recognition requires the visibility of major facial features for face normalization, orientation correction, and recognition. Thus, the chapter focuses on the facial recognition based on the feature points surrounding the eye region rather than taking the whole face as a parameter.

Face Recognition Through Multi-Resolution Images

In this paper, we introduce a new method for face recognition in multi-resolution images. The proposed method is composed of two phases: an off-line phase and an inference phase. In the off-line phase, we built the Kernel Partial Least Squares (KPLS) regression model to map the LR facial features to HR ones. The KPLS predictor was then used in the inference phase to map HR features from LR features. We applied in both phases the Block-Based Discrete Cosine Transform (BBDCT) descriptor to enhance the facial feature description. Finally, the identity matching was carried out with the K-Nearest Neighbor (KNN) classifier. Experimental study was conducted on the AR and ORL databases and the obtained results proved the efficiency of the proposed method to deal with LR and VLR face recognition problem.

Eye of Devil: Face Recognition in Real World Surveillance Video with Feature Extraction and Pattern Matching

People lost, people missing etc., these are the words we come across whenever there is any mass gathering events going on or in crowded areas. To solve this issue some traditional approaches like announcements are in use. One idea is to identify the person using face recognition and pattern matching techniques. There are several techniques to implement face recognition like extraction of facial features by using the position of eyes, nose, jawbone or skin texture analysis etc., By using these techniques a unique dataset can be created for each human. Here the photograph of the missing person can be used to extract these facial features. After getting the dataset of that individual, by using pattern matching techniques, there is a scope to find the person with same facial features in the crowd images or videos. Keywords: Face-Recognition, Image-Processing, Feature extraction, Video-Processing, Pattern-Matching.