Big-data DWT (Discrete Wavelet Transform) robust watermarking-based anti-illumination attack face recognition method

Abstract

The invention discloses a big-data DWT (Discrete Wavelet Transform) robust watermarking-based anti-illumination attack face recognition method, which mainly comprises a watermarking embedding part and a watermarking extraction part, in the watermarking extraction part, the face recognition is realized simultaneously; the face recognition method mainly comprises the following steps: watermarking embedding: I) firstly, carrying out wavelet transform to all original faces, then carrying out DFT (Discrete Fourier Transform) on approximation coefficient to obtain a characteristic vector; II) enabling the watermarking of each face to be correlated with the characteristic vector of the face through a cryptography hash function; watermarking extraction: III) acquiring the characteristic vector of a to-be-measured face, thereby obtaining the maximum value of the correlation coefficient of the characteristic vector of the to-be-measured face and the original face, and according to the value, completing the face recognition and acquiring the corresponding embedded watermarking; and IV) carrying out watermarking extraction by utilizing the characteristic vectors of the to-be-measured face, and calculating the correlation coefficient of the watermarking. The face recognition method does not need sample training, and is suitable for big data, as well as can better resist attack from illumination, shielding and the like.

Description

technical field [0001] The invention relates to the field of multimedia signal processing, in particular to a face recognition method based on DWT robust watermarking against light attack under big data. technical background [0002] As an effective biometric identification technology, face recognition technology has been paid more and more attention by industry and academia in the past 40 years. Because face recognition technology has the advantages of being highly acceptable, natural, and not easy to be detected, it has great uses in entertainment, criminal investigation, access control systems, and military affairs. [0003] At present, face recognition methods are mainly based on machine learning methods such as PCA, neural network, and SVM. Due to the need for training and learning, the recognition samples are relatively large. In the big data environment, the learning time is relatively long, and the current face Recognition methods are sensitive to illumination chang...

AI Technical Summary

Problems solved by technology

At present, there are few researches on face recognition methods based on big data environment, anti-illumination and occlusion attacks, and no public reports have been seen.

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