It is difficult to extract suitable face feature for classification, and the face recognition accuracy is low under unconstrained condition. To solve the above problems, a new method based on deep neural network and weighted fusion of face features, namely DLWF, was proposed. First, facial feature points were located by using Active Shape Model (ASM), then different organs of face were sampled according to those facial feature points. The corresponding Deep Belief Network (DBN) was trained by the regional samples to get optimal network parameters. Finally, the similarity vector of different organs was obtained by using Softmax regression. The weighted fusion of multiple regions in the similarity vector method was used for face recognition. The recognition accuracy got to 97% and 88.76% respectively on the ORL and LFW face database; compared with the traditional recognition algorithm including Principal Components Analysis (PCA), Support Vector Machine (SVM), DBN, and Face Identity-Preserving (FIP) + Linear Discriminant Analysis (LDA), no matter under the constrained condition or the unconstrained condition, recognition rates were both improved. The experimental results show that the proposed algorithm has high efficiency in face recognition.

At present, the accuracy of image retrieval is a difficult problem to study, the main reason is the method of feature extraction. In order to improve the precision of image retrieval, a new image retrieval method based on multi-feature called CAUC (Comprehensive Analysis based on the Underlying Characteristics) was presented. First, based on YUV color space, the mean value and the standard deviation were used to extract the global feature from an image that depicted the global characteristics of the image, and the image bitmap was introduced to describe the local characteristics of the image. Secondly, the compactness and Krawtchouk moment were extracted to describe the shape features. Then, the texture features were described by the improved four-pixel co-occurrence matrix. Finally, the similarity between images was computed based on multi-feature fusion, and the images with high similarity were returned.On Corel-1000 image set, the comparative experiments with method which only considered four-pixel co-occurrence matrix showed that the retrieval time of CAUC was greatly reduced without significantly reducing the precision and recall. In addition, compared with the other two kinds of retrieval methods based on multi-feature fusion, CAUC improved the precision and recall with high retrieval speed. The experimental results demonstrate that CAUC method is effective to extract the image feature, and improve retrieval efficiency.

To solve the problem that present traversal methods of hierarchical tree which lead to low efficiency, a new collision detection algorithm based on classified traversal was proposed. Firstly, these objects were classified according to the difference between the balance factors of two tree' nodes. The simultaneous depth-first traversal method was applied to the objects which have similar structure, and the commutative depth-first traversal method was applied to the other objects, which reduced the number of intersect tests. Then, the process of traversal was optimized by using the temporal spatial coherence and priority strategy. Finally, the experimental results show that, compared with the collision detection algorithm based on unified traversal, the proposed algorithm shortens the time of the intersection test. The bigger the number of objects, the more significant the advantage of quickness, it can reduce about 1/5 of the required time.