Focusing on the issue that the phase consistency feature is deficient in detection of vascular center, a new retinal vessel segmentation algorithm based on hybrid phase feature was proposed. Firstly, an original retinal image was preprocessed. Secondly, every pixel was represented by a 4-D vector composed of Hessian matrix, Gabor transformation, Bar-selective Combination Of Shifted FIlter REsponses (B-COSFIRE) and phase feature. Finally, Support Vector Machine (SVM) was used for pixel classification to realize the segmentation of retinal vessels. Among the four features, phase feature was a new hybrid phase feature formed by phase consistency feature and Hessian matrix feature through wavelet fusion. This new phase feature not only preserves good vascular edge information by phase consistency feature, but also compensates for the deficient detection of vascular center by phase consistency feature. The average Accuracy (Acc) of the proposed algorithm evaluated on the Digital Retinal Images for Vessel Extraction (DRIVE) database is 0.9574, and the average Area Under receiver operating characteristic Curve (AUC) is 0.9702. In the experiment of using single feature for vessel extraction through pixel classification, compared with using phase consistency feature, using hybrid phase feature for vessel extraction improves the average Accuracy (Acc) from 0.9191 to 0.9478, the AUC from 0.9359 to 0.9702. The experimental results show that hybrid phase feature is more suitable for retinal vessel segmentation based on pixel classification than phase consistency feature.

Aiming at the problem that the lip feature dimension is too high and sensitive to the scale space, a technique based on the Scale-Invariant Feature Transform (SIFT) algorithm was proposed to carry out the speaker authentication. Firstly, a simple video frame image neat algorithm was proposed to adjust the length of the lip video to the same length, and the representative lip motion pictures were extracted. Then, a new algorithm based on key points of SIFT was proposed to extract the texture and motion features. After the integration of Principal Component Analysis (PCA) algorithm, the typical lip motion features were obtained for authentication. Finally, a simple classification algorithm was presented according to the obtained features. The experimental results show that compared to the common Local Binary Pattern (LBP) feature and the Histogram of Oriental Gradient (HOG) feature, the False Acceptance Rate (FAR) and False Rejection Rate (FRR) of the proposed feature extraction algorithm are better, which proves that the whole speaker lip motion recognition algorithm is effective and can get the ideal results.