Clothing identification via deep learning:forensic applications

Attribute-based identification systems are essential for forensic investigations because they help in identifying individuals.An item such as clothing is a visual attribute because it can usually be used to describe people.The method proposed in this article aims to identify people based on the visual information derived from their attire.Deep learning is used to train the computer to classify images based on clothing content.We first demonstrate clothing classification using a large scale dataset,where the proposed model performs relatively poorly.Then,we use clothing classification on a dataset containing popular logos and famous brand images.The results show that the model correctly classifies most of the test images with a success rate that is higher than 70%.Finally,we evaluate clothing classification using footage from surveillance cameras.The system performs well on this dataset,labelling 70%of the test images correctly.

Evaluating OpenFace:an open-source automatic facial comparison algorithm for forensics

This article studies the application of models of OpenFace(an open-source deep learning algorithm)to forensics by using multiple datasets.The discussion focuses on the ability of the software to identify similarities and differences between faces based on images from forensics.Experiments using OpenFace on the Labeled Faces in the Wild(LFW)-raw dataset,the LFW-deep funnelled dataset,the Surveillance Cameras Face Database(SCface)and ForenFace datasets showed that as the resolution of the input images worsened,the effectiveness of the models degraded.In general,the effect of the quality of the query images on the efficiency of OpenFace was apparent.Therefore,OpenFace in its current form is inadequate for application to forensics,but can be improved to offer promising uses in the field.

Critical review of the use and scientific basis of forensic gait analysis

This review summarizes the scientific basis of forensic gait analysis and evaluates its use in the Netherlands,United Kingdom and Denmark,following recent critique on the admission of gait evidence in Canada.A useful forensic feature is(1)measurable,(2)consistent within and(3)different between individuals.Reviewing the academic literature,this article found that(1)forensic gait features can be quantified or observed from surveillance video,but research into accuracy,validity and reliability of these methods is needed;(2)gait is variable within individuals under differing and constant circumstances,with speed having major influence;(3)the discriminative strength of gait features needs more research,although clearly variation exists between individuals.Nevertheless,forensic gait analysis has contributed to several criminal trials in Europe in the past 15 years.The admission of gait evidence differs between courts.The methods are mainly observer-based:multiple gait analysts(independently)assess gait features on video footage of a perpetrator and suspect.Using gait feature databases,likelihood ratios of the hypotheses that the observed individuals have the same or another identity can be calculated.Automated gait recognition algorithms calculate a difference measure between video clips,which is compared with a threshold value derived from a video gait recognition database to indicate likelihood.However,only partly automated algorithms have been used in practice.We argue that the scientific basis of forensic gait analysis is limited.However,gait feature databases enable its use in court for supportive evidence with relatively low evidential value.The recommendations made in this review are(1)to expand knowledge on inter-and intra-subject gait variabilities,discriminative strength and interdependency of gait features,method accuracies,gait feature databases and likelihood ratio estimations;(2)to compare automated and observer-based gait recognition methods;to design(3)an international standard method with known validity,reliability and proficiency tests for analysts;(4)an international standard gait feature data collection method resulting in database(s);(5)(inter)national guidelines for the admission of gait evidence in court;and(6)to decrease the risk for cognitive and contextual bias in forensic gait analysis.This is expected to improve admission of gait evidence in court and judgment of its evidential value.Several ongoing research projects focus on parts of these recommendations.

Google timeline accuracy assessment and error prediction

Google Location Timeline,once activated,allows to track devices and save their locations.This feature might be useful in the future as available data for evidence in investigations.For that,the court would be interested in the reliability of these data.The position is presented in the form of a pair of coordinates and a radius,hence the estimated area for tracked device is enclosed by a circle.This research focuses on the assessment of the accuracy of the locations given by Google Location History Timeline,which variables affect this accuracy and the initial steps to develop a linear multivariate model that can potentially predict the actual error with respect to the true location considering environmental variables.The determination of the potential influential variables(configuration of mobile device connectivity,speed of movement and environment)was set through a series of experiments in which the true position of the device was recorded with a reference Global Positioning System(GPS)device with a superior order of accuracy.The accuracy was assessed measuring the distance between the Google provided position and the de facto one,later referred to as Google error.If this Google error distance is less than the radius provided,we define it as a hit.The configuration that has the largest hit rate is when the mobile device has GPS available,with a 52%success.Then the use of 3G and 2G connection go with 38%and 33%respectively.The Wi-Fi connection only has a hit rate of 7%.Regarding the means of transport,when the connection is 2G or 3G,the worst results are in Still with a hit rate of 9%and the best in Car with 57%.Regarding the prediction model,the distances and angles from the position of the device to the three nearest cell towers,and the categorical(nonnumerical)variables of Environment and means of transport were taking as input variables in this initial study.To evaluate the usability of a model,a Model hit is defined when the actual observation is within the 95%confidence interval provided by the model.Out of the models developed,the one that shows the best results was the one that predicted the accuracy when the used network is 2G,with 76%of Model hits.The second model with best performance had only a 23%success(with the mobile network set to 3G).

Camera recognition with deep learning

In this paper,camera recognition with the use of deep learning technique is introduced.To identify the various cameras,their characteristic photo-response non-uniformity(PRNU)noise pattern was extracted.In forensic science,it is important,especially for child pornography cases,to link a photo or a set of photos to a specific camera.Deep learning is a sub-field of machine learning which trains the computer as a human brain to recognize similarities and differences by scanning it,in order to identify an object.The innovation of this research is the use of PRNU noise patterns and a deep learning technique in order to achieve camera identification.In this paper,AlexNet was modified producing an improved training procedure with high maximum accuracy of 80%–90%.DIGITS showed to have identified correctly six cameras out of 10 with a success rate higher than 75%in the database.However,many of the cameras were falsely identified indicating a fault occurring during the procedure.A possible explanation for this is that the PRNU signal is based on the quality of the sensor and the artefacts introduced during the production process of the camera.Some manufacturers may use the same or similar imaging sensors,which could result in similar PRNU noise patterns.In an attempt to form a database which contained different cameras of the same model as different categories,the accuracy rate was low.This provided further proof of the limitations of this technique,since PRNU is stochastic in nature and should be able to distinguish between different cameras from the same brand.Therefore,this study showed that current convolutional neural networks(CNNs)cannot achieve individualization with PRNU patterns.Nevertheless,the paper provided material for further research.

Digital, big data and computational forensics

Recent years have witnessed significant developments in deep learning and artificial intelligence[1].For instance,remarkable improvements have been made in automated face comparison systems by using deep learning,compared with the classic approaches[2].The term“deep learning”is often used to refer to certain kinds of neural networks.The first publications on biological neural networks and the brain date back to the late 1800s[3].It was not until the rediscovery of the back-propagation algorithm[4]in 1986 that interest in the field was reignited.An artificial neural network is designed following a simple modelling of the brain,and involves a representation of neurons.A neuron receives a specific signal and converts to a different one.Neurons can also be used to learn from examples.They adjust a transfer function.Many neurons are linked together,and are often used in multiple layers.A visual overview is provided in Figure 1.An example of the application of neural networks is face recognition[5],where these networks examine images of people’s faces and find features,such as shapes of nose,ears,and mouth.In such networks,the parameters of thousands or more neurons are adjusted based on training to improve recognition performance.Combined with improved pattern recognition to detect the eyes,mouth,and the position of the face,they yield better results.