Designing an Adaptive Age-Invariant Face Recognition System for Enhanced Security in Smart Urban Environments

The advent of smart technology in urban environments has often been hailed as the solution to a plethora of contemporary urban challenges, ranging from environmental conservation to waste management and transportation. However, the critical aspect of security, encompassing crime detection and prevention, is frequently overlooked. Moreover, there is a dearth of research exploring the potential disruption of conventional face detection and recognition systems by new smart city surveillance security cameras, particularly those which autonomously update their databases. This paper addresses this gap by proposing the enhancement of security in smart cities through the development of an adaptive Age-Invariant Face Recognition (AIFR) model. A non-intrusive AIFR model was constructed using a convolutional neural network and transfer learning techniques, and was then integrated into surveillance cameras. These cameras, designed to capture the faces of city residents at regular intervals, consequently updated their databases autonomously. Upon testing, the developed model demonstrated its potential to substantially improve security by effectively detecting and identifying the residents and visitors of smart cities, and updating their database profiles. Remarkably, the model retained its effectiveness even with significant age intra-class variation, with the capability to alert relevant authorities about potential criminals or missing individuals. This research underscores the potential of adaptive face recognition systems in bolstering security measures within smart urban environments

Comparative analysis of augmented datasets performances of age invariant face recognition models

The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%

The Role of Data Quality and Heterogeneity on the Calibration of Neural Networks

Neural networks have been widely studied and used in recent years due to its highclassification accuracy and training efficiency. With the increase of network depth, however,the models become worse calibrated, meaning they cannot reflect the true probabilities. Onthe other hand, in many applications such as medical diagnosis, facial recognition and selfdriving cars, the calibrated output probabilities are of critical importance. Therefore, theunderstanding of the cause of deep neural network uncalibration is of much concern.The influence of model structures on the output calibration has been explored.However, the impact of the training dataset quality and heterogeneity, such as dataset sizeand label noise remains unclear. In this thesis, the impact of data quality and heterogeneityon the output calibration is investigated theoretically and experimentally. Afterwards, thedefect of calibration methods using single global parameter are discussed. To overcomethe calibration issues resulting from the dataset heterogeneity, we propose an improvedcalibration technique that can give better performance

A Reliability-Aware Approach for Resource Efficient Virtual Network Function Deployment

OAPA Network function virtualization (NFV) is a promising technique aimed at reducing capital expenditures (CAPEX) and operating expenditures (OPEX), and improving the flexibility and scalability of an entire network. In contrast to traditional dispatching, NFV can separate network functions from proprietary infrastructure and gather these functions into a resource pool that can efficiently modify and adjust service function chains (SFCs). However, this emerging technique has some challenges. A major problem is reliability, which involves ensuring the availability of deployed SFCs, namely, the probability of successfully chaining a series of virtual network functions (VNFs) while considering both the feasibility and the specific requirements of clients, because the substrate network remains vulnerable to earthquakes, floods and other natural disasters. Based on the premise of users & #x2019; demands for SFC requirements, we present an Ensure Reliability Cost Saving (ER & #x005F;CS) algorithm to reduce the CAPEX and OPEX of telecommunication service providers (TSPs) by reducing the reliability of the SFC deployments. The results of extensive experiments indicate that the proposed algorithms perform efficiently in terms of the blocking ratio, resource consumption, time consumption and the first block

An improved age invariant face recognition using data augmentation

In spite of the significant advancement in face recognition expertise, accurately recognizing the face of the same individual across different ages still remains an open research question. Face aging causes intra-subject variations (such as geometric changes during childhood & adolescence, wrinkles and saggy skin in old age) which negatively affects the accuracy of face recognition systems. Over the years, researchers have devised different techniques to improve the accuracy of age invariant face recognition (AIFR) systems. In this paper, the face and gesture recognition network (FG-NET) aging dataset was adopted to enable the benchmarking of experimental results. The FG-Net dataset was augmented by adding four different types of noises at the preprocessing phase in order to improve the trait aging face features extraction and the training model used at the classification stages, thus addressing the problem of few available training aging for face recognition dataset. The developed model was an adaptation of a pre-trained convolution neural network architecture (Inception-ResNet-v2) which is a very robust noise. The proposed model on testing achieved a 99.94% recognition accuracy, a mean square error of 0.0158 and a mean absolute error of 0.0637. The results obtained are significant improvements in comparison with related works