Realization Of Internet Retrieval Technology Based On Face Recognition

With the rapid development of Internet technology,the types of Internet applications are also diversifying.In life,users always use search engines to achieve the acquisition of specific face images such as celebrities and stars.However,the traditional text-based image retrieval has the disadvantages that the retrieval method is single and the results are easily deceived by keywords.And the new content-based image retrieval has the drawbacks of insufficient retrieval accuracy and the process being interfered to unrelated images.Both of these methods cannot satisfy the user's need for searching for a specific face image on the Internet.Internet retrieval system based on face recognition is a network image search engine that comprehensively utilizes the related knowledge of Internet technology,image retrieval and face recognition.The main contents of the dissertation are:1.The face detection part uses the Ada Boost face detection algorithm to quickly filter out Internet images that do not contain faces in the early stage of system data collection to improve the face retrieval efficiency.At the same time,for the filtered image,the trained AdaBoost face classifier is used to accurately extract the face part of the image,excluding the interference of the background area of the image.2.The image pretreatment part focuses on the problems of rotation and lightingchanges in the face.For face rotation,an accurate filter that responds well to the position of the human eye is constructed using the ASEF algorithm to achieve accurate positioning of the human eye,and then the image is rotated to obtain a human face with the same posture.For non-uniform illumination,the homomorphic filter is used to perform illumination compensation on the image,which attenuates the low-frequency part of the image(light changes)and enhances the high-frequency part of the image(the details of the human face),thereby reducing the adverse effects of changes in illumination on subsequent links.3.Feature extraction and similarity matching part uses LBP operator to extract features of face images.Through the histogram statistics of binary pattern numbers in the image processed by LBP operator,a feature vector that accurately describes the local face texture distribution is obtained.With the cosine-distance-based similarity matching algorithm,it quickly and intuitively discriminates the similarity between two faces.4.Integrate the above three links and use the tool web crawler,implementing an internet retrieval system based on face recognition in Matlab simulation platform.In the performance test,the retrieval system achieved an average recognition rate of90% and 60% on the two datasets with better and worse sample quality.