With the rise in vehicular population in developing countries like India, parking these vehicles has proven to be challenge to many. This paper describes a low-cost solution that alleviates the parking problem. The paper identifies the availability of parking space as the problem and system described provides an efficient way to utilize the available space. Parking zones can adopt sensors to detect an empty space in parking zones. It uses modern wireless communication technology to transmit and receive sensor data at respective ends, this data is then stored in the cloud and a user can realize the availability of an empty space through a mobile app at real time. The system is decoupled and presented in such a way many parking zones can be added and removed without affecting the core functionality of the system. The utilization of cloud technologies ensures high reliability, availability.
Face appearance drastically changes under varying background, pose and illumination conditions. Face Recognition (FR) in such varying conditions becomes a difficult and challenging task. In this paper, we propose three novel techniques, viz., Face Detection based on 8-Connectivity-of-Skin-Region (FDCSR), Standard Deviation based Pose Detection (SDPD) and Gamma Ray Burst Rhombus Star (GRBRS) feature mask to improve the performance of FR systems. FDCSR is used as a preprocessing step to remove cluttered background from the image. SDPD is also a preprocessing step where pose neutralization technique is employed. GRBRS feature mask on the Fast Fourier Transform (FFT) of the preprocessed image is used to extract the salient features of the face. Binary Particle Swarm Optimization (BPSO) feature selection algorithm is used to search the feature vector space for the optimal feature subset. Experimental results show promising performance of the proposed techniques for FR on four benchmark face databases, namely, Color FERET, CMUPIE, HP and FEI.
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