Automatic emotion recognition from the analysis of body movement has tremendous potential to revolutionize virtual reality, robotics, behavior modeling, and biometric identity recognition domains. A computer system capable of recognizing human emotion from the body can also significantly change the way we interact with the computers. One of the significant challenges is to identify emotion-specific features from a vast number of descriptors of human body movements. In this paper, we introduce a novel two-layer feature selection framework for emotion classification from a comprehensive list of body movement features. We used the feature selection framework to accurately recognize five basic emotions: happiness, sadness, fear, anger, and neutral. In the first layer, a unique combination of Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) was utilized to eliminate irrelevant features. In the second layer, a binary chromosome-based genetic algorithm was proposed to select a feature subset from the relevant list of features that maximizes the emotion recognition rate. Score and rank-level fusion were applied to further improve the accuracy of the system. The proposed system was validated on proprietary and public datasets, containing 30 subjects. Different action scenarios, such as walking and sitting actions, as well as an action-independent case, were considered. Based on the experimental results, the proposed emotion recognition system achieved a very high emotion recognition rate outperforming all of the state-of-the-art methods. The proposed system achieved recognition accuracy of 90.0% during walking, 96.0% during sitting, and 86.66% in an action-independent scenario, demonstrating high accuracy and robustness of the developed method.

Cloud computing is a technology that has gained rapid popularity in recent years. It has enabled use of immense computational power in a scalable and cost-efficient manner. Deployment of biometric technology in government and commercial organizations has become a standard security practice. However, independent biometric systems tend to be computationally and financially expensive, especially when user enrollment is high. A feasible solution is to create a biometric system on the cloud which can be used ubiquitously as an authentication service. In this paper, we propose a first cancelable biometric framework based on deep learning on the cloud. We establish that cloud is a good solution for biometric systems where intensive computation, quick response times, and high accuracy is required.

Recognizing author identity from digital footprints without having a large corpus of documents from an individual is of keen interest to security researchers and government agencies. Users reveal aspects of their personality via the content they share with their social media followers and through the patterns in their interactions on online networking platforms. This study examines the potency of emerging natural language processing (NLP) methods in analyzing social network activity. A linguostylistic personality traits assessment (LPTA) system is developed to estimate Twitter users’ personality traits based on their tweets using the Myers-Briggs-type indicator (MBTI) and big-five personality scales. A novel input representation mechanism is proposed to process tweets by converting them into real-valued vectors using frequency, co-occurrence, and context (FCC) measures. Other prevalent text representation schemes, such as one-hot encoding, count-based vectorization, and pretrained language model representations are used as comparators. A genetic algorithm (GA) approach is proposed to reduce the feature set and increase the efficacy of the features extracted. The developed system outperforms the state-of-the-art research by reliably estimating the user’s latent personality traits while using 50 or fewer tweets per user.