Abstract
Affected by factors such as attitude, light, expression, etc., it is impossible to accurately identify the identity in a wireless visual sensor network in an uncontrollable environment. In traditional visual identity recognition, it is necessary to convert uncontrollable factors into controllable and stable feature factors for identity recognition in a relatively uncontrollable environment where the node distribution is relatively complicated. The conversion process leads to long recognition time and low efficiency. An adaptive recognition method for identity features in wireless visual sensing networks based on LBP face recognition is proposed. A strong classifier is obtained for cascading, and the underlying features are extracted. The final Harr face cascade classifier is applied to the face Check it out. The PCA dimensionality reduction processing of the facial area feature vector is performed to obtain the low-dimensional feature vector, the dimensionality reduction coefficient, and the average face of the person. For the face image in the wireless local area, its LBP operation is given. Perform histogram statistics on face feature information, obtain face LBP histograms, and perform feature matching on the face feature database to complete recognition. The improved algorithm has improved the cumulative matching score of traditional algorithms by 17.8%; the accuracy rate has improved It is 32.7%, and the recognition time is shortened by 3.9s. Simulation results show that the proposed algorithm has high accuracy and recognition efficiency.
Highlights
As wireless visual sensor network development and the application of information security people put forward higher requirements [1]
Identity adaptive knowledge is the key to the theoretical basis of authorization, recognition algorithm research topics have become the focus of scholars in the field, it has been more and more attention [4, 5]
The paper by the singular value decomposition theorems obtained eigenvalues, λi (i = 1, 2,⋯, n) used to describe, The eigen values are arranged in descending order, find the corresponding eigenvalues and orthogonal normalized eigenvectors, φ1,φ2,⋯,φn used to describe, Demand can select last λ1 ≥ λ2 ≥ ⋯ ≥ λn Eigen face space formed, the sample can be any of a human face is projected onto the face feature space
Summary
As wireless visual sensor network development and the application of information security people put forward higher requirements [1]. The literature [6] Proposed single feature identity recognition algorithm, based on a certain facial features to identify the identity of the algorithm has fast calculation speed, real-time advantage, but the affected light, rotation, etc., in a complex context, recognition rate will be reduced; literature [7] Proposed identity recognition algorithm combined features will not be fixed in accordance with the same characteristics as the principles together, and this algorithm is more efficient overall, but the information is too large, resulting in reduced real-time recognition; literature [8] Proposed common vector combined identity 2 DPCA special algorithm, through Uram-Schmidt orthogonal transformation implemented on the same vector image is calculated, based on the results obtained to identify the minimum distance detection, the algorithm has a good discrimination function, but complexity is too high, low recognition efficiency; literature [9] Proposed a three-dimensional model of facial expression recognition algorithm identity of deformed face implementation model registration, high accuracy, there are common face model and real-time problem of poor matching model registration it can not be obtained; literature [10] SFM's proposed identity recognition algorithm, the first search match point, after SFM algorithm determined the three-dimensional. Experimental results demonstrate that the proposed algorithm has high recognition accuracy and efficiency
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