No abstract

Nowadays, facial recognition combined with age estimation and gender prediction has been deeply involved with the factors associated with crowd monitoring. This is considered to be a major and complex job for humans. This paper proposes a unified facial recognition system based on already available deep learning and machine learning models (i.e., FaceNet, ResNet, Support Vector Machine, AgeNet and GenderNet) that automatically and simultaneously performs person identification, age estimation and gender prediction. Then the system is evaluated on a newly proposed multi-face, realistic and challenging test dataset. The current face recognition technology primarily focuses on static datasets of known identities and does not focus on novel identities. This approach is not suitable for continuous crowd monitoring. In our proposed system, whenever novel identities are found during inference, the system will save those novel identities with an appropriate label for each unique identity and the system will be updated periodically in order to correctly recognise those identities in the future inference iterations. However, extracting the facial features of the whole dataset whenever a new identity is detected is not an efficient solution. To address this issue, we propose an incremental feature extraction based training method which aims to reduce the computational load of feature extraction. When tested on the proposed test dataset, our proposed system correctly recognizes pre-trained identities, estimates age, and predicts gender with an average accuracy of 49%, 66.5% and 93.54% respectively. We conclude that the evaluated pre-trained models can be sensitive and not robust to uncontrolled environment (e.g., abrupt lighting conditions).

No abstract

Evacuation procedures are an integral aspect of the emergency response strategy of a hospital. Evacuation simulation models help to properly evaluate and improve evacuation strategies. However, the issue of exit selection during evacuation is often overlooked and oversimplified in the evacuation simulation models. Moreover, most of the available evacuation simulation models lack integration of movement devices and assisted evacuation features. However, finding a solution of these limitations is a necessity to properly evaluate evacuation strategies. To tackle this problem, we propose an effective approach to model exit selection using a fuzzy logic controller (FLC) and simulate assisted hospital evacuation using Unity3D game engine. Our research demonstrates that selecting exits based on distance only is not sufficient for real life situation because it ignores the unpredictability of human behavior. On the contrary, the use of the proposed FLC for exit selection makes the simulation more realistic by addressing the uncertainty and randomness in an evacuee's decision-making process. This research can play a vital role in future developments of evacuation simulation models.