Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve sa...Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.展开更多
MicroRNAs are an important subclass of non-coding RNAs (ncRNA), and serve as main players into RNA interference (RNAi). Mature microRNA derived from stem-loop structure called precursor. Identification of precursor mi...MicroRNAs are an important subclass of non-coding RNAs (ncRNA), and serve as main players into RNA interference (RNAi). Mature microRNA derived from stem-loop structure called precursor. Identification of precursor microRNA (pre-miRNA) is essential step to target microRNA in whole genome. The present work proposed 25 novel local features for identifying stem- loop structure of pre-miRNAs, which captures characteristics on both the sequence and structure. Firstly, we pulled the stem of hairpins and aligned the bases in bulges and internal loops used ‘―’, and then counted 24 base-pairs (‘AA’, ‘AU’, …, ‘―G’, except ‘――’) in pulled stem (formalized by length of pulled stem) as features vector of Support Vector Machine (SVM). Performances of three classifiers with our features and different kernels trained on human data were all superior to Triplet-SVM-classifier’s in po- sitive and negative testing data sets. Moreover, we achieved higher prediction accuracy through combining 7 global sequence-structure. The result indicates validity of novel local features.展开更多
基金National High Technology Research and Development Program (863) of China(2007AA01Z334)National Natural Science Foundation of China(69903006,60373065,0721002)New Century Excellent Talents in University(NCET-04-0460)
基金Supported by the Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(XCXJH20220318)。
文摘Since the outbreak of Coronavirus Disease 2019(COVID-19),people are recommended to wear facial masks to limit the spread of the virus.Under the circumstances,traditional face recognition technologies cannot achieve satisfactory results.In this paper,we propose a face recognition algorithm that combines the traditional features and deep features of masked faces.For traditional features,we extract Local Binary Pattern(LBP),Scale-Invariant Feature Transform(SIFT)and Histogram of Oriented Gradient(HOG)features from the periocular region,and use the Support Vector Machines(SVM)classifier to perform personal identification.We also propose an improved Convolutional Neural Network(CNN)model Angular Visual Geometry Group Network(A-VGG)to learn deep features.Then we use the decision-level fusion to combine the four features.Comprehensive experiments were carried out on databases of real masked faces and simulated masked faces,including frontal and side faces taken at different angles.Images with motion blur were also tested to evaluate the robustness of the algorithm.Besides,the experiment of matching a masked face with the corresponding full face is accomplished.The experimental results show that the proposed algorithm has state-of-the-art performance in masked face recognition,and the periocular region has rich biological features and high discrimination.
文摘MicroRNAs are an important subclass of non-coding RNAs (ncRNA), and serve as main players into RNA interference (RNAi). Mature microRNA derived from stem-loop structure called precursor. Identification of precursor microRNA (pre-miRNA) is essential step to target microRNA in whole genome. The present work proposed 25 novel local features for identifying stem- loop structure of pre-miRNAs, which captures characteristics on both the sequence and structure. Firstly, we pulled the stem of hairpins and aligned the bases in bulges and internal loops used ‘―’, and then counted 24 base-pairs (‘AA’, ‘AU’, …, ‘―G’, except ‘――’) in pulled stem (formalized by length of pulled stem) as features vector of Support Vector Machine (SVM). Performances of three classifiers with our features and different kernels trained on human data were all superior to Triplet-SVM-classifier’s in po- sitive and negative testing data sets. Moreover, we achieved higher prediction accuracy through combining 7 global sequence-structure. The result indicates validity of novel local features.
