By combining the AdaBoost modular locality preserving projection(AMLPP) algorithm and the locally linear regression(LLR) algorithm,a novel pose-invariant algorithm is proposed to realize high-accuracy face recognition under different poses.In the training stage of this algorithm,the AMLPP is employed to select the crucial frontal blocks and construct effective strong classifier.According to the selected frontal blocks and the corresponding non-frontal blocks,LLR is then applied to learn the linear mappings which will be used to convert the non-frontal blocks to visual frontal blocks.During the testing of the learned linear mappings,when a non-frontal face image is inputted,the non-frontal blocks corresponding to the selected frontal blocks are extracted and converted to the visual frontal blocks.The generated virtual frontal blocks are finally fed into the strong classifier constructed by AMLPP to realize accurate and efficient face recognition.Our algorithm is experimentally compared with other pose-invariant face recognition algorithms based on the Bosphorus database.The results show a significant improvement with our proposed algorithm.
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机译:LTE通讯系统中针对同层干扰环境对微小型基地台功率控制与用户位置推荐演算法 =Femtocell Power Control and User Location Recommendation Algorithm for Co-Tier Interference Environment in LTE Communication System