Biblio

Human emotion recognition plays a vital role in interpersonal communication and human-machine interaction domain. Emotions are expressed through speech, hand gestures and by the movements of other body parts and through facial expression. Facial emotions are one of the most important factors in human communication that help us to understand, what the other person is trying to communicate. People understand only one-third of the message verbally, and two-third of it is through non-verbal means. There are many face emotion recognition (FER) systems present right now, but in real-life scenarios, they do not perform efficiently. Though there are many which claim to be a near-perfect system and to achieve the results in favourable and optimal conditions. The wide variety of expressions shown by people and the diversity in facial features of different people will not aid in the process of coming up with a system that is definite in nature. Hence developing a reliable system without any flaws showed by the existing systems is a challenging task. This paper aims to build an enhanced system that can analyse the exact facial expression of a user at that particular time and generate the corresponding emotion. Datasets like JAFFE and FER2013 were used for performance analysis. Pre-processing methods like facial landmark and HOG were incorporated into a convolutional neural network (CNN), and this has achieved good accuracy when compared with the already existing models.

The hardware and low-level software in many mobile devices are capable of mobile-to-mobile communication, including ad-hoc 802.11, Bluetooth, and cognitive radios. We have started to leverage this capability to provide interpersonal communication both over infrastructure networks (the Internet), and over ad-hoc and delay-tolerant networks composed of the mobile devices themselves. This network is decentralized in the sense that it can function without any infrastructure, but does take advantage of infrastructure connections when available. All interpersonal communication is encrypted and authenticated so packets may be carried by devices belonging to untrusted others. The decentralized model of security builds a flexible trust network on top of the social network of communicating individuals. This social network can be used to prioritize packets to or from individuals closely related by the social network. Other packets are prioritized to favor packets likely to consume fewer network resources. Each device also has a policy that determines how many packets may be forwarded, with the goal of providing useful interpersonal communications using at most 1% of any given resource on mobile devices. One challenge in a fully decentralized network is routing. Our design uses Rendezvous Points (RPs) and Distributed Hash Tables (DHTs) for delivery over infrastructure networks, and hop-limited broadcast and Delay Tolerant Networking (DTN) within the wireless ad-hoc network.