Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep...Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.展开更多
Touch deoxyribonucleic acid(DNA)refers to the DNA that is left behind from skin cells when a person touches or comes into contact with an object.In crimes where the identification of suspects becomes a challenge,touch...Touch deoxyribonucleic acid(DNA)refers to the DNA that is left behind from skin cells when a person touches or comes into contact with an object.In crimes where the identification of suspects becomes a challenge,touch DNA has been a proven investigative tool.The present study aims to provide a systematic review of the role of touch DNAin criminal cases which discusses the nature and importance of touch DNA evidence at crime scenes;various phenomena including the transfer and persistence of touched samples;different factors affecting the touch sample deposition and DNA shedding;the best recovery methods and collection of samples from different substrates;and the interpretation of profiles through advanced techniques that identify the suspects.The present study also aims to optimize standard protocols in the laboratories for touched samples appropriate to the substrates that improve the success rate of profiles from touched items.展开更多
文摘Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.
文摘Touch deoxyribonucleic acid(DNA)refers to the DNA that is left behind from skin cells when a person touches or comes into contact with an object.In crimes where the identification of suspects becomes a challenge,touch DNA has been a proven investigative tool.The present study aims to provide a systematic review of the role of touch DNAin criminal cases which discusses the nature and importance of touch DNA evidence at crime scenes;various phenomena including the transfer and persistence of touched samples;different factors affecting the touch sample deposition and DNA shedding;the best recovery methods and collection of samples from different substrates;and the interpretation of profiles through advanced techniques that identify the suspects.The present study also aims to optimize standard protocols in the laboratories for touched samples appropriate to the substrates that improve the success rate of profiles from touched items.
