This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of obtaining hand-drawn sketch datasets,there is a lack of a complete benchmark for assessing the developme...This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of obtaining hand-drawn sketch datasets,there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade.We first introduce a high-quality dataset for FSS,named FS2K,which consists of 2104 image-sketch pairs spanning three types of sketch styles,image backgrounds,lighting conditions,skin colors,and facial attributes.FS2K differs from previous FSS datasets in difficulty,diversity,and scalability and should thus facilitate the progress of FSS research.Second,we present the largest-scale FSS investigation by reviewing 89 classic methods,including 25 handcrafted feature-based facial-sketch synthesis approaches,29 general translation methods,and 35 image-to-sketch approaches.In addition,we elaborate comprehensive experiments on the existing 19 cutting-edge models.Third,we present a simple baseline for FSS,named FSGAN.With only two straightforward components,i.e.,facialaware masking and style-vector expansion,our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin.Finally,we conclude with lessons learned over the past years and point out several unsolved challenges.Our code is available at https://github.com/DengPingFan/FSGAN.展开更多
Presently,suspect prediction of crime scenes can be considered as a classification task,which predicts the suspects based on the time,space,and type of crime.Performing digital forensic investigation in a big data env...Presently,suspect prediction of crime scenes can be considered as a classification task,which predicts the suspects based on the time,space,and type of crime.Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer.Besides,the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes.The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program(composite sketches)based on the verbal explanation given by the eyewitness or victim.Since this suspect identification process is slow and difficult,it is required to design a technique for a quick and automated facial sketch generation.Machine Learning(ML)and deep learning(DL)models find it useful to automatically support the decision of forensics experts.The challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better decisions.In this view,this study develops a new artificial intelligence(AI)based DL model with face sketch synthesis(FSS)for suspect identification(DLFSS-SI)in a big data environment.The proposed method performs preprocessing at the primary stage to improvise the image quality.In addition,the proposed model uses a DL based MobileNet(MN)model for feature extractor,and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization(QOFFO)algorithm.The proposed model automatically draws the sketches of the input facial images.Moreover,a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the eyewitness.If there is a higher resemblance between the two sketches,the suspect will be determined.To validate the effective performance of the DLFSS-SI method,a detailed qualitative and quantitative examination takes place.The experimental outcome stated that the DLFSSSI model has outperformed the compared methods in terms of mean square error(MSE),peak signal to noise ratio(PSNR),average actuary,and average computation time.展开更多
基金supported by the Grant-in-Aid for Japan Society for the Promotion of Science Fellows, Japan (No. 21F50377)
文摘This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of obtaining hand-drawn sketch datasets,there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade.We first introduce a high-quality dataset for FSS,named FS2K,which consists of 2104 image-sketch pairs spanning three types of sketch styles,image backgrounds,lighting conditions,skin colors,and facial attributes.FS2K differs from previous FSS datasets in difficulty,diversity,and scalability and should thus facilitate the progress of FSS research.Second,we present the largest-scale FSS investigation by reviewing 89 classic methods,including 25 handcrafted feature-based facial-sketch synthesis approaches,29 general translation methods,and 35 image-to-sketch approaches.In addition,we elaborate comprehensive experiments on the existing 19 cutting-edge models.Third,we present a simple baseline for FSS,named FSGAN.With only two straightforward components,i.e.,facialaware masking and style-vector expansion,our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin.Finally,we conclude with lessons learned over the past years and point out several unsolved challenges.Our code is available at https://github.com/DengPingFan/FSGAN.
文摘Presently,suspect prediction of crime scenes can be considered as a classification task,which predicts the suspects based on the time,space,and type of crime.Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer.Besides,the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes.The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program(composite sketches)based on the verbal explanation given by the eyewitness or victim.Since this suspect identification process is slow and difficult,it is required to design a technique for a quick and automated facial sketch generation.Machine Learning(ML)and deep learning(DL)models find it useful to automatically support the decision of forensics experts.The challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better decisions.In this view,this study develops a new artificial intelligence(AI)based DL model with face sketch synthesis(FSS)for suspect identification(DLFSS-SI)in a big data environment.The proposed method performs preprocessing at the primary stage to improvise the image quality.In addition,the proposed model uses a DL based MobileNet(MN)model for feature extractor,and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization(QOFFO)algorithm.The proposed model automatically draws the sketches of the input facial images.Moreover,a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the eyewitness.If there is a higher resemblance between the two sketches,the suspect will be determined.To validate the effective performance of the DLFSS-SI method,a detailed qualitative and quantitative examination takes place.The experimental outcome stated that the DLFSSSI model has outperformed the compared methods in terms of mean square error(MSE),peak signal to noise ratio(PSNR),average actuary,and average computation time.
