The rapid development of internet and social media has driven the great requirement for information sharing and intelligent property protection. Therefore, reversible information embedding theory has marked some approaches for information security. Assuming reversibility, the original and embedded data must be completely restored. In this paper, a high-capacity and multilayer reversible information hiding technique for digital images was presented. First, the integer Haar wavelet transform scheme converted the cover image from the spatial into the frequency domain that was used. Furthermore, we applied dynamic threshold analysis, the parameters of the predicted model, the location map, and the multilayer embedding method to improve the quality of the stego image and restore the cover image. In comparison with current algorithms, the proposed algorithm often had better embedding capacity versus image quality performance.
Human biometrics is a popular method for security applications of personal identification. In past years, most research topics focused on face, iris, and fingerprint or signature recognition. Apart from these features, human noses can also be used as a biometric feature for gender classification according to our daily experience. In this work, four distinct features are extracted from nose images to distinguish between male and female faces. By comparing with other nose features obtained from 3D stereoscopic photographs, the proposed features are easily extracted from ordinary face images. Image preprocessing methods are applied to extract those four features automatically, and the linear discriminant analysis (LDA) method is applied to classify those extracted features for gender classification. Experimental results demonstrate that average classification accuracy can reach to 77%. In particular, the curvature feature calculated from the nose wing achieves the best classification performance.
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