In machine vision, surveillance systems are a kind of security that concentrates on the safety of the human and property. One of the main tasks of a surveillance system is the detection of humans. This paper presents a system of human detection and the development of a technique of human segmentation using a combination of information thermal and depth in a real indoor setting from a mobile robot. A novel fusion of thermal-depth information (FTDI) is introduced to enhance the efficiency of the segmentation process and expedite processing. In experimental studies, evaluation of the performance for the proposed system is carried out using Ground Truth (GT), in which the proposed system yield is compared to GT. The proposed system performs well with an approximate accuracy of over 90% for all data sets as illustrated in the quantitative results and even outperformed state-of-the-art algorithms. This paper presents the novelty of the work, in which the detection method can improve the classification of persons and their occlusion. The advantages, such as being computationally inexpensive and performs well even under severe occlusion and poor illumination, show that this proposed system is robust.
The typical scheme used to generated cryptographic key is a fuzzy extractor. The fuzzy extractor is the extraction of a stable data from biometric data or noisy data based on the error correction code (ECC) method. Forward error correction includes two ways are blocked and convolutional coding used for error control coding. "Bose_Chaudhuri_Hocquenghem" (BCH) is one of the error correcting codes employ to correct errors in noise data. In this paper use fuzzy extractor scheme to find strong key based on BCH coding, face recognition data used SVD method and hash function. Hash_512 converted a string with variable length into a string of fixed length, it aims to protect information against the threat of repudiation.
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