Research On License Plate Detection And Face Detection Base On Deep Convolution Neural Networks

Detection of face and the license has been a hot issue in the field of target detection. Based on the application and effect of face recognition and license plate recognition play in real life is more important, especially with the development of Internet technology and intelligent technology, people in data mining, security monitoring, intelligent transportation and other related intelligent systems have increasingly strong demand. In reality, all kinds of image acquisition system has been popular, produced a huge amount of license plate, the face image information. The collected images often come from different environments and system, resulting in no uniform format of data, so that the subsequent information extraction and mining face many difficulties. At the same time, unrestricted environment makes the positioning is faced with many complicated factors, such as uneven illumination, pose, motion blur, low resolution, decorative objects occlusions. Although their research started earlier, but the current detection system for a certain environmental constraints. At the same time, the license plate, the problem of face detection has a lot in common, so the research of license plate and the face detection problem is of great significance and practical value.Early detection of license plate and the face, try positioning using visual features of color, texture, geometry contour of targets. The analysis of these methods can clearly see all these methods have their respective advantages and disadvantages and the applicable scenes. Monitoring system in fixed locations, data often has the advantages of high resolution, single fixed characteristics, from the perspective of the scene. In the specific environment of this limited, we use the method of artificial design features, can be highly efficient and well solve the problem of the two kind. However, such as outdoor surveillance system, there is motion blur, decoration, environment factors such as occlusion interference data. In the natural environment, the use of artificial design features a single, combined method of performance are seriously affected by the. Therefore, to enhance the robustness of license plate, the face detection algorithm for different scenarios, interference factors, is a very worthy of study and solve technical problems.Since twentieth Century 90 years, along with the human brain cognitive in-depth, the statistical machine learning method based on the proposed after it has been fully developed and perfected. The statistical based machine learning methods to dig out the rich characteristics of the sample data from said that in many aspects, compared with manual design features have shown great superiority. This method greatly stimulated the research on theory and application of machine learning enthusiasm and confidence. Neural network is one of the best machine learning algorithm, by simulating the human visual characteristics of the nervous system, the distributed parallel processing algorithm of the mathematical model, using the back propagation algorithm(Back Propagation) is the MNIST classification problem of a good solution for training artificial neural network model. Convolutional neural network is an artificial neural network. The global training model, through sample training autonomous learning from the original input shift invariant feature, the sliding window technology makes the algorithm can accomplish the image scanning with low cost based, so in the field of target detection has been widely used. After 2006, with the depth of learning concept and the development of the research, especially in speech recognition, image classification and other fields, the deep learning model is a good solution to the shallow learning model of insurmountable obstacles, greatly promoted the new wave, depth of machine learning. In this paper, the depth of learning is proposed based on neural network using convolutional(Deep Convolutional Neural Networks) model to solve the problem of face detection in complex environment and vehicle license plate and a good performance on the set of the experimental data obtained.