Focused on the potential safety hazard of leakage of fingerprint and other biometrics in the cloud environment, as well as the lack of security or convenience of the existing biometric authentication schemes, a terminal APP of trusted fingerprint authentication based on orthogonal decomposition and TrustZone was designed and implemented. The sensitive operations such as fingerprint feature extraction, fingerprint template generation were executed in the trusted execution environment provided by the hardware isolation mechanism of TrustZone, making these operations isolated from the applications in the general execution environment to resist the attacks of malicious programs and ensure the security of the authentication process. The fingerprint template generated on the basis of orthogonal decomposition algorithm integrate the random noise while remaining the matching ability, so that it was able to resist the attack against the feature template to a certain extent. As a result, the fingerprint template was able to be stored and transmitted in the cloud environment, so that the user and the device were unbound, which improved the convenience of biometric authentication. Experiments and theoretical analysis show that the correlation and randomness of the fingerprint template of the proposed algorithm is higher than those of original feature and random projection algorithms, so that the algorithm has stronger security. In addition, the experimental results of time and storage overheads as well as recognition accuracy show that, both convenience and security are considered in this APP, meeting the requirements of security authentication in mobile cloud environment.

Concerning the problem that it is difficult to estimate the shape characteristics of irregular objects, a new automatic detection method for irregular jewelry images was put forward by introducing the concept of Principal Component Analysis (PCA) to realize the automatic measurement for jewelry. First, the principal axis of target image was extracted by PCA. Then, four vertices of the external rectangle of jewelry were computed according to the optimization direction of the principal axis. Last, the best-fitted rectangle of irregular contour was positioned to detect the irregular shape of the jewelry. The proposed method was applied to real jewelry images, experimental results illustrate that this algorithm can accurately locate the target in the image. Compared with the linear spectral frequency method and the projection rotation translation method, the subjective and objective evaluation results prove the superiority of the proposed algorithm.

A new corner detection algorithm based on the autocorrelation matrix of Multi-channel Odd Gabor grAdient (MOGA) was proposed to suppress the decrease of corner positioning accuracy caused by the smoothed edge. The input image was transformed by 8-channel odd Gabor filter, and then autocorrelation matrices were constructed for each pixel by Gabor gradient correlation of the pixel and its surrounding pixels. If the sum of the normalized eigenvalues of the pixel was local maxima, the pixel was labeled as a corner. Compared with the classical algorithms, such as Harris and Curvature Scale Space (CSS), the proposed algorithm increased the average rate of correct detection by 17.74%, and decreased the average rate of positioning error by 18.15%. The experimental results show that the proposed algorithm has very good detection performance, and gets higher corner detection rate and better corner positioning accuracy.