The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, baseline method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.

Stress is one of the most severe concerns in modern life. High-level stress can create various diseases or loss of focus and productivity at work. Being under stress prevents people from recognizing their stress levels, so early stress detection is essential. Recently, multimodal fusion has enhanced the performance of stress detection models using Deep Learning (DL) techniques. The low, mid, and high-level features of a Convolutional Neural Network (CNN) are discriminative. A comprehensive feature representation can be obtained by fusing all three levels of CNN’s features. This study mainly focuses on detecting stress by exploiting these advantages using a multimodal hierarchical CNN feature fusion. The two multimodal physiological signals used in this study are Electrodermal activity (EDA) and Electrocardiogram (ECG). We develop a hierarchical feature set by concatenating multi-level CNN features for each modality. Multimodal fusion on both hierarchical feature sets is performed using the Multimodal Transfer Module (MMTM). The experiments are carried out with raw frequency domain data and the features from the frequency bands to study the effectiveness of both. The model’s performance is compared to the different combinations of hierarchical features from low, mid, and high levels. To verify the generalizability, the proposed approach has been evaluated on four benchmark datasets- ASCERTAIN, CLAS, MAUS, and WAUC. The proposed method showed its effectiveness by outperforming existing models by 1-2%, respectively, on frequency band features. It is observed that the hierarchical feature set from all three levels performed better than all other combinations by 2-4%. As a result, this strategy can be a useful addition to stress detection.

This paper presents the recognition of handwritten Hindi Characters based on the modified exponential membership function fitted to the fuzzy sets derived from features consisting of normalized distances obtained using the Box approach. The exponential membership function is modified by two structural parameters that are estimated by optimizing an objective function that includes the entropy and error function. A Reuse Policy that provides guidance from the past policies is utilized to improve the reinforcement learning. This relies on the past errors exploiting the past policies. The Reuse Policy improves the speed of convergence of the learning process over the strategies that learn without reuse and combined with the use of the reinforcement learning, there is a 25-fold improvement in training. Experimentation is carried out on a database of 4750 samples. The overall recognition rate is found to be 90.65%.

Diagnosis of Autism Spectrum Disorder (ASD) using clinical evaluation (cognitive tests) is challenging due to wide variations amongst individuals. Since no effective treatment exists, prompt and reliable ASD diagnosis can enable the effective preparation of treatment regimens. This paper proposes structural Magnetic Resonance Imaging (sMRI)-based ASD diagnosis via an outlier detection approach. To learn spatio-temporal patterns in structural brain connectivity, a Generative Adversarial Network (GAN) is trained exclusively with sMRI scans of healthy subjects. Given a stack of three adjacent slices as input, the GAN generator reconstructs the next three adjacent slices; the GAN discriminator then identifies ASD sMRI scan reconstructions as outliers. This model is compared against two other baselines– a simpler UNet and a sophisticated Self-Attention GAN. Axial, Coronal and Sagittal sMRI slices from the multi-site ABIDE II dataset are used for evaluation. Extensive experiments reveal that our ASD detection framework performs comparably with the state-of-the-art with far fewer training data. Furthermore, longitudinal data (two scans per subject over time) achieve 17-28% higher accuracy than cross-sectional data (one scan per subject). Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.