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Corrupted face image is one of the important obstacles that machine vision systems encounter when trying to recognize faces. In this paper, we propose a new face recognition system that can deal with the problem of corrupted images more efficiently. The new technique applies the principal component analysis to the phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the images. It will be shown that the proposed technique increases the face recognition rate when applied to images of low resolution and corrupted by noise, compared to other known methods.

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Numerous face recognition techniques have been developed owing to the growing number of real-world applications. Most of current algorithms for face recognition involve considerable amount of computations and hence they cannot be used on devices constrained with limited speed and memory. In this paper, we propose a novel solution for efficient face recognition problem for systems that utilize small memory capacities and demand fast performance. The new technique divides the face images into components and finds the discriminant phases of the Fourier transform of these components automatically using the sequential floating forward search method. A thorough study and comprehensive experiments relating time consumption versus system performance are applied to benchmark face image databases. Finally, the proposed technique is compared with other known methods and evaluated through the recognition rate and the computational time, where we achieve a recognition rate of 98.5% with computational time of 6.4 minutes for a database consisting of 2360 images.

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The automotive industry is rapidly accelerating toward the development of innovative industry applications that feature management capabilities for data and applications alike in cars. In this regard, more internet of vehicles solutions are emerging through advancements of various wireless medium access-control technologies and the internet of things. In the present work, we develop a short-range communication–based vehicular system to support vehicle communication and remote car control. We present a combined hardware and software testbed that is capable of controlling a vehicle’s start-up, operation and several related functionalities covering various vehicle metric data. The testbed is built from two microcontrollers, Arduino and Raspberry Pi 3, each of which individually controls certain functions to improve the overall vehicle control. The implementation of the heterogeneous communication module is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 and IEEE 802.15 medium access control technologies. Further, a control module on a smartphone was designed and implemented for efficient management. Moreover, we study the system connectivity performance by measuring various important parameters including the coverage distance, signal strength, download speed and latency. This study covers the use of this technology setup in different geographical areas over various time spans.