The current standard Unified Modeling Language(UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A n...The current standard Unified Modeling Language(UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A new UML profile that may customize UML for framework modeling was presented using the extension mechanisms of UML, providing a group of UML extensions to meet the needs of framework modeling. In this profile, the extended class diagrams and sequence diagrams were defined to straightforwardly identify the hot-spots and describe their instantiation restrictions. A transformation model based on design patterns was also put forward, such that the profile based framework design diagrams could be automatically mapped to the corresponding implementation diagrams. It was proved that the presented profile makes framework modeling more straightforwardly and therefore easier to understand and instantiate.展开更多
This paper presents a new technique of unified probabilistic models for facerecognition from only one single example image per person. The unified models, trained on anobtained training set with multiple samples per p...This paper presents a new technique of unified probabilistic models for facerecognition from only one single example image per person. The unified models, trained on anobtained training set with multiple samples per person, are used to recognize facial images fromanother disjoint database with a single sample per person. Variations between facial images aremodeled as two unified probabilistic models: within-class variations and between-class variations.Gaussian Mixture Models are used to approximate the distributions of the two variations and exploita classifier combination method to improve the performance. Extensive experimental results on theORL face database and the authors'' database (the ICT-JDL database) including totally 1,750 facialimages of 350 individuals demonstrate that the proposed technique, compared with traditionaleigenface method and some well-known traditional algorithms, is a significantly more effective androbust approach for face recognition.展开更多
Human faces have two important characteristics: (1) They are similar objectsand the specific variations of each face are similar to each other; (2) They are nearly bilateralsymmetric. Exploiting the two important prop...Human faces have two important characteristics: (1) They are similar objectsand the specific variations of each face are similar to each other; (2) They are nearly bilateralsymmetric. Exploiting the two important properties, we build a unified model in identity subspace(UMIS) as a novel technique for face recognition from only one example image per person. An identitysubspace spanned by bilateral symmetric bases, which compactly encodes identity information, ispresented. The unified model, trained on an obtained training set with multiple samples per classfrom a known people group A, can be generalized well to facial images of unknown individuals, andcan be used to recognize facial images from an unknown people group B with only one sample persubject, Extensive experimental results on two public databases (the Yale database and the Berndatabase) and our own database (the ICT-JDL database) demonstrate that the UMIS approach issignificantly effective and robust for face recognition.展开更多
文摘The current standard Unified Modeling Language(UML) could not model framework flexibility and extendibility adequately due to lack of appropriate constructs to distinguish framework hot-spots from kernel elements. A new UML profile that may customize UML for framework modeling was presented using the extension mechanisms of UML, providing a group of UML extensions to meet the needs of framework modeling. In this profile, the extended class diagrams and sequence diagrams were defined to straightforwardly identify the hot-spots and describe their instantiation restrictions. A transformation model based on design patterns was also put forward, such that the profile based framework design diagrams could be automatically mapped to the corresponding implementation diagrams. It was proved that the presented profile makes framework modeling more straightforwardly and therefore easier to understand and instantiate.
文摘This paper presents a new technique of unified probabilistic models for facerecognition from only one single example image per person. The unified models, trained on anobtained training set with multiple samples per person, are used to recognize facial images fromanother disjoint database with a single sample per person. Variations between facial images aremodeled as two unified probabilistic models: within-class variations and between-class variations.Gaussian Mixture Models are used to approximate the distributions of the two variations and exploita classifier combination method to improve the performance. Extensive experimental results on theORL face database and the authors'' database (the ICT-JDL database) including totally 1,750 facialimages of 350 individuals demonstrate that the proposed technique, compared with traditionaleigenface method and some well-known traditional algorithms, is a significantly more effective androbust approach for face recognition.
文摘Human faces have two important characteristics: (1) They are similar objectsand the specific variations of each face are similar to each other; (2) They are nearly bilateralsymmetric. Exploiting the two important properties, we build a unified model in identity subspace(UMIS) as a novel technique for face recognition from only one example image per person. An identitysubspace spanned by bilateral symmetric bases, which compactly encodes identity information, ispresented. The unified model, trained on an obtained training set with multiple samples per classfrom a known people group A, can be generalized well to facial images of unknown individuals, andcan be used to recognize facial images from an unknown people group B with only one sample persubject, Extensive experimental results on two public databases (the Yale database and the Berndatabase) and our own database (the ICT-JDL database) demonstrate that the UMIS approach issignificantly effective and robust for face recognition.
