Vehicle analysis involves license-plate recognition (LPR), vehicle-type classification (VTC), and vehicle make and model recognition (MMR). Among these tasks, MMR plays an important complementary role in respect to LPR. In this paper, we propose a novel framework for MMR using local tiled deep networks. The frontal views of vehicle images are first extracted and fed into the local tiled deep networks for training and testing. A local tiled convolutional neural network (LTCNN) is proposed to alter the weight sharing scheme of CNN with local tiled structure. The LTCNN unties the weights of adjacent units and then ties the units k steps from each other within a local map. This architecture provides the translational, rotational, and scale invariance as well as locality. In addition, to further deal with the colour and illumination variation, we applied the histogram oriented gradient (HOG) to the frontal view of images prior to the LTCNN. The experimental results show that our LTCNN framework achieved a 98% accuracy rate in terms of vehicle MMR.

Cross-pose face recognition with only a single frontal gallery face is a challenging task.Multiple virtual views are generated from a single frontal gallery face in advance.Alignment error of two faces is calculated after a two-phase (pose and individual) alignment.Correlation between patches is considered by overlapping and covariance.Cross-pose face recognition accuracy is significantly improved than compared methods. Although studied for decades, effective face recognition remains difficult to accomplish on account of occlusions and pose and illumination variations. Pose variance is a particular challenge in face recognition. Effective local descriptors have been proposed for frontal face recognition. When these descriptors are directly applied to cross-pose face recognition, the performance significantly decreases. To improve the descriptor performance for cross-pose face recognition, we propose a face recognition algorithm based on multiple virtual views and alignment error. First, warps between poses are learned using the Lucas-Kanade algorithm. Based on these warps, multiple virtual profile views are generated from a single frontal face, which enables non-frontal faces to be matched using the scale-invariant feature transform (SIFT) algorithm. Furthermore, warps indicate the correspondence between patches of two faces. A two-phase alignment error is proposed to obtain accurate warps, which contain pose alignment and individual alignment. Correlations between patches are considered to calculate the alignment error of two faces. Finally, a hybrid similarity between two faces is calculated; it combines the number of matched keypoints from SIFT and the alignment error. Experimental results show that our proposed method achieves better recognition accuracy than existing algorithms, even when the pose difference angle was greater than 30¿. Display Omitted