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 reco...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.展开更多
Neuropsychological impairment has long been established as a fundamental characteristic of depression,but a generally accepted, specific pattern of neuropsychological impairment has not been summarized. In this study,...Neuropsychological impairment has long been established as a fundamental characteristic of depression,but a generally accepted, specific pattern of neuropsychological impairment has not been summarized. In this study,we examined the classic neuropsychological paradigm of self-face recognition, to explore whether the self was impaired in major depressive disorder(MDD). Eighteen MDD patients and 20 healthy subjects were recruited to participate in this study. By using a face morphing technique, we measured the size of processing bias in MDD patients during different face discrimination tasks relative to controls. Results of analysis of variance(ANOVA) showed a significant main effect of Group(F(1, 36)= 7.388, P =0.01). Subsequent independent t-tests further revealed that self bias(t = 2.636, P = 0.012) and self-recognition bias(t = 2.190, P = 0.035) observed in self-famous task and self-stranger task respectively for patients were significantly greater than that for controls. Both self-processing and selfrecognition were impaired in patients with MDD, indicating that MDD individuals might exist level of self-abnormalities. These findings provide a new perspective for further study on the etiological and pathological mechanisms of MDD.展开更多
基金Supported by the National Natural Science Foundation of China(60772066)
文摘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.
基金supported by Beijing EducationCommittee Funds(1102013501)
文摘Neuropsychological impairment has long been established as a fundamental characteristic of depression,but a generally accepted, specific pattern of neuropsychological impairment has not been summarized. In this study,we examined the classic neuropsychological paradigm of self-face recognition, to explore whether the self was impaired in major depressive disorder(MDD). Eighteen MDD patients and 20 healthy subjects were recruited to participate in this study. By using a face morphing technique, we measured the size of processing bias in MDD patients during different face discrimination tasks relative to controls. Results of analysis of variance(ANOVA) showed a significant main effect of Group(F(1, 36)= 7.388, P =0.01). Subsequent independent t-tests further revealed that self bias(t = 2.636, P = 0.012) and self-recognition bias(t = 2.190, P = 0.035) observed in self-famous task and self-stranger task respectively for patients were significantly greater than that for controls. Both self-processing and selfrecognition were impaired in patients with MDD, indicating that MDD individuals might exist level of self-abnormalities. These findings provide a new perspective for further study on the etiological and pathological mechanisms of MDD.
