Local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods hav...
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Local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods have limited accuracy because of their threshold value. One problem is earning one value for two different regions with a diverse pixel neighbourhood, which causes mistakes in feature vector and decreases the discriminative power. In this study, the authors proposed a modified LBP that covers the LBP's disadvantages. The proposed approach is arithmetic coded LBP (ACLBP) that uses arithmetic coding process during LBP calculation instead of applying original thresholds. The proposed policy addresses the problem of returning one similar LBP value for two different patches. Moreover, the proposed method modifies LBP by using a different threshold for calculating the pixels differences. Using this algorithm, the authors conducted a genetic-based feature fusion method by combining LBP and histogram of oriented gradients and ACLBP. The proposed approach could work better on LFW dataset, and the ORL dataset and Yale face dataset that shows the improving role of ACLBP in comparison with the earlier version of LBP.
A Method of perceiving a human face through innovation is cleared by facerecognition. face identification and recognition have been utilized in the security frameworks, access control which has acquired sufficient ub...
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ISBN:
(纸本)9781665421119;9781665421126
A Method of perceiving a human face through innovation is cleared by facerecognition. face identification and recognition have been utilized in the security frameworks, access control which has acquired sufficient ubiquity over the most recent couple of years. Biometrics is utilized to plan facial highlights from a photo or video in a facial recognition framework. A database of known faces is used to compare the information to find a match. Facial recognition can verify personal identity, but it also raises privacy issues. This paper compares the different facial recognitionalgorithms and reviews them. We aim to come up with an approach that best suits a database and gives high accuracy. This paper intends to focus on different facerecognition methods and gives a brief view of the application. It also gives a comparative study of various parameters for the measurement of the performance of an algorithm for a particular database system, Yale Database, and ORL Database. The comparative study on different approaches depicts each algorithm's performance and its limitations.
High dynamic range (HDR) imaging has been developed for improved visual representation by capturing a wide range of luminance values. Owing to its properties, HDR content might lead to a larger privacy intrusion, requ...
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High dynamic range (HDR) imaging has been developed for improved visual representation by capturing a wide range of luminance values. Owing to its properties, HDR content might lead to a larger privacy intrusion, requiring new methods for privacy protection. Previously, false colours were proved to be effective for assuring privacy protection for low dynamic range (LDR) images. In this work, the reliability of false colours when used for privacy protection of HDR images represented by tone-mapping operators (TMOs) is studied. Two different TMO techniques are tested, a simple TMO based on the Gamma transform and a more complex local TMO. Moreover, two false colour palettes are also tested, and are applied to images that result from both TMOs and also to an LDR image that represents the centre exposure in the image sequence used to create the HDR image. The degree of privacy protection is analysed through both a subjective test using crowdsourcing and an objective test using face recognition algorithms. It is concluded that the application of the two studied false colour palettes reduces the recognition accuracy with respect to both tests.
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