Identification Of Suspects Research Articles

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

A rate-them-all lineup procedure increases information but reduces discriminability.

Prior research has investigated ways to optimize identification performance, but an open question concerns exactly what variable should be optimized. One reasonable way to optimize performance is to maximize discriminability, which is achieved by increasing correct identifications of guilty suspects while simultaneously decreasing false identifications of innocent suspects. Another reasonable way to optimize performance is to maximize the information about the guilt or innocence of the suspect, which is best achieved by ensuring that a confidence rating is always made to the suspect. In a typical lineup, however, limited information about the suspect is obtained if the witness picks a filler or rejects the lineup. One proposed solution to that problem is to have the witness provide a confidence rating to every member of the lineup (a rate-them-all lineup). But what effect, if any, does a rate-them-all procedure have on discriminability? To answer that question, we compared a rate-them-all lineup procedure to standard simultaneous lineup and showup procedures using receiver operating characteristic analysis. In terms of discriminability, the rate-them-all procedure was diagnostically inferior to both. A reasonable goal for future research is to make use of theoretical models of eyewitness identification to simultaneously maximize both discriminability and information gain. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparative Analysis of FaceNet, VGGFace, and GhostFaceNets Face Recognition Algorithms For Potential Criminal Suspect Identification

The escalating concerns surrounding criminal activities underscore the imperative for bolstered security measures to safeguard public welfare. Despite concerted efforts, the identification of suspects remains fraught with limitations, hindering the attainment of comprehensive individual profiles. Leveraging advancements in facial detection and identification technologies, this study assesses the efficacy of three prominent deep learning models—FaceNet, VGGFace, and GhostFaceNets—in the domain of facial recognition for suspect identification. Drawing upon data collected in 2023, the investigation scrutinizes FaceNet's intricate methodologies, including triplet loss optimization and Euclidean space mappings, yielding exceptional accuracy rates of 97.05% during validation and 97.4% during testing. Conversely, VGGFace, while displaying commendable accuracies, registers marginally lower accuracy metrics, standing at 97.05% and 96.1% during validation and testing, respectively. GhostFaceNets, integrating novel architectural components, exhibit diminished accuracy rates, signaling avenues for refinement. These empirical insights underscore FaceNet's prowess in furnishing robust and reliable facial recognition outcomes, while delineating the imperative for iterative enhancements in GhostFaceNets to foster their pragmatic applicability in security domains.

Face Detection and Recognition for Criminal Identification System

This project showcases a powerful Face Detection and Recognition system intended to improve law enforcement skills by speeding up the identification of suspects in criminal cases. The system uses scalable databases and real-time computation, however instead of using traditional web sources for face detection. Accuracy and dependability are ensured by using the LBPH algorithm for face recognition and the Haar-cascade classifier for feature detection. The smtplib library allows the system to send a Gmail notification when it finds a matching face. The system that has been built contributes to public safety initiatives by improving law enforcement operations and demonstrating versatility for different face detection circumstances. The outcomes show how successfully the system can detect faces in real-time circumstances. The system achieves great performance by creating dynamic datasets and integrating algorithms. Integrating Gmail improves law enforcement response by facilitating prompt contact of identified suspects. This study highlights the role that surveillance technology plays in public safety and crime prevention, and it provides law enforcement authorities with useful solutions. Furthermore, the uses of this facial recognition and detection technology go beyond police enforcement. It can expedite attendance tracking and improve security in access control systems. Because of its adaptability, it can be used in a variety of industries and is expected to have a significant impact.

Forensic Significance of Touch Deoxyribonucleic Acid

Abstract 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 DNA in 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.

Psychological distress and coping skills used by individuals in the crisis caused by the COVID-19 pandemic.

Psychological distress, as a key indicator, describes a person's emotional problems and psychological reactions to adapt to the environment. This study aimed to investigate the relationship between psychological distress and coping strategies used by individuals during the crisis caused by COVID-19. The current research is an analytical cross-sectional study that was designed in 2021. The sampling area included the three cities of Lar, Gerash, and Evaz in the south of Fars province in the south of Iran. The study population was the residents of these three cities during the COVID-19 pandemic. The final sample size in the present study was 384 people. Individuals were selected through available sampling. By answering questions from the self-assessment system of COVID-19 disease, the Ministry of Health and Medical Education filled out the necessary information for individual screening and registration of the symptoms of COVID-19 disease. Then, they completed the Coping Methods Questionnaire and the Psychological Distress Questionnaire. The mean age of the participants was 40.90 years. One hundred sixty-seven (43.5%) participants were male, and 217 (56.5%) were female. The mean score of problem-oriented coping style was significantly different between the levels of education (P = 0.001). The mean score of problem-solving coping style for the participants suspected of being infected with COVID-19 was 6.18 scores lower than others, and the difference was statistically significant (P = 0.001). Multiple regression with a forward selection also showed that among the studied variables, age, problem-oriented, and emotion-oriented coping strategies were important variables affecting mental distress. People who were not suspected of having COVID-19 symptoms had a higher mean problem-oriented coping score than those who were suspected of having COVID-19. Therefore, early identification of suspects and the implementation of counseling and training programs can be very effective.

Phenotypic mismatch between suspects and fillers but not phenotypic bias increases eyewitness identifications of Black suspects.

Despite converging evidence that people more closely associate the construct of criminality with Black people who exhibit a more African facial phenotype than Black people who express a more European phenotype, eyewitness researchers have largely ignored phenotypic bias as a potential contributor to the racial disparities in the criminal legal system. If this form of phenotypic bias extends to eyewitness identification tasks, eyewitnesses may be more likely to identify Black suspects with an African rather than European phenotype, regardless of their guilt status. Further, in cases where the witness's description of the perpetrator does not contain phenotypic information, phenotypic mismatch between the suspect and the other lineup members may bias identification decisions toward or against the suspect. If witnesses can use elements of the lineup construction to guide their identification decisions rather than relying on their recognition memory, then the lineup should be deemed unfair due to suspect bias. The current study also investigated lineup presentation method as a procedural safeguard, predicting that that when lineups were presented simultaneously, there would be a significant two-way interaction of phenotypic bias and lineup composition, with a larger simple main effect of phenotypic bias when lineups were suspect-biased (i.e., the fillers were a phenotypic mismatch to the suspect) than when all lineup members shared the same phenotype. We expected that this interaction would be significantly smaller or non-significant for sequential lineups. Participants watched a mock crime video that contained a Black culprit with either a more African phenotype or a less African phenotype before attempting identifications from a photo array that contained a suspect whose phenotype always matched the culprit viewed in the video, but varied in culprit-presence, phenotypic match of the suspect and fillers, and presentation method. Participants did not identify Black suspects with Afrocentric features more often than Black suspects with Eurocentric features. However, witnesses made more identifications of suspects when the fillers did not match the suspect's phenotype compared to when all lineup members possessed similar phenotypic features. In sum, phenotypic bias did not influence our participant-witnesses' identification decisions, nor interact with lineup composition and lineup presentation type to affect identifications of suspects, suggesting that phenotypic bias may be less influential in match-to-memory tasks than other types of legal decision-making (e.g., determining guilt and sentencing). However, the suggestiveness created by failing to match fillers' phenotypes to the suspect's phenotype can be avoided with proper attention to fair lineup construction.

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.

Performance evaluation of generative adversarial networks for generating mugshot images from text description

The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.

Classification and Identification of Individuals Using Analysis Lip Prints

Identifying individuals is a major challenge for forensic investigators, as DNA and fingerprints are highly valuable, but are not always readily available at crime scenes. Lip prints could be used in this context since they are unique to each individual. Lip-print study (Cheiloscopy) is a reliable biometric technology and is considered a unique parameter for identification. This study was determined lip print patterns among samples of the Salah El-Din population in Iraq and distribution their pattern type of lip print by using a program in MATLAB. In this study, it was established that there are significant differences between populations, countries, and genders when it comes to the distribution of lip prints. Materials and Methods: A total of participants was 100 (50 females & 50 males) their ages more than 10 years old. The lip print was divided into six quadrants on white copy paper then examined with technology using the program in MATLAB to determine the classification of lip print types for pattern distribution. Lip print was recorded by the usage of red natural color and healthy, white copy papers, and tissue paper, the obtained prints were first examined and later analyzed and interpreted.Results: This study discovered that no two samples from the same family have identical lip-print patterns. Females had the greatest recorded pattern of groove type I (long vertical grooves), whereas males had the highest recorded pattern of groove type II (branched grooves). This pattern was most observed in the lower middle region. The present study proved that lip prints of males and females are different types in different countries.Conclusion: lip prints are unique for every individual even in between the family. Hence, Lip prints play a vital role as a supplementary tool in the personal identification of suspects or unknown identity.

Pivotal role of CHWs in prevention, early detection and control of outbreaks: neglected key primary players; a call to action

Pivotal role of CHWs in prevention, early detection and control of outbreaks: neglected key primary players; a call to action

The History of Forensic Science in India by Saumitra Basu

Reviewed by: The History of Forensic Science in India by Saumitra Basu Saurav Kumar Rai (bio) The History of Forensic Science in India By Saumitra Basu. London and New York: Routledge, 2021. Pp. xvii + 204. "Forensic science," the application of science for purposes of law and justice, entails the uses of biology, physics, chemistry, and general scientific principles and methods to solve legal problems. Thus, legal setup and requirements play a crucial role in the evolution of forensic science. In India, argues Saumitra Basu in The History of Forensic Science in India, the rise, growth, and development of forensic science can be located in the colonial period (p. 19). The British Raj not only drastically altered the precolonial concept of crime and criminal justice but also revolutionized the nature and magnitude of criminal investigation. Extensive use of forensic science in criminal jurisprudence was part and parcel of this phenomenon. However, instead of beginning his narrative directly in the colonial period, the author starts with a discussion of the beginning of crime identification in different civilizations. Thus, in the first chapter, he begins with the notion of crime and punishment and associated investigations in ancient India with special attention focused on toxicology (pp. 27–56). Here, Basu's primary focus is on agada tantra, the branch of Ayurveda (a traditional Indian healing system) that deals with various kinds of toxins, ways to clean them out of the body, and antidotes for particular poisons. Similarly, when discussing crime and justice in ancient India, the prime attention is on the Arthashastra, a comprehensive administrative manual. However interesting these discussions are, they do not connect well with the central theme of the book, as forensic science was not used in ancient India (p. 45). Basu does, however, believe that crime was detected according to scientific observation and analysis, as delineated in agada tantra and the Arthashastra. This discussion seems to reveal the author's love for India's ancient past more than historical rigor. This assessment also applies to the second chapter, which looks at long-term developments in criminal investigation in West Asia, China, Rome, Egypt, England, America, and Latin America. In fact, investigation of any crime by its very nature includes the application of some scientific methods to reach any conclusion. However, it should not be treated as a precursor of forensic science, which is very much a modern discipline, even though the word "forensic" can be traced to the Latin word forum, or the public square where cases were heard and debated based on certain evidence. This is like [End Page 584] arguing for the premodern presence of biotechnology by taking examples of past practices of the fermentation of grains to produce beer. From chapter 3 onward, the author deals with the development of forensic science in colonial India. The colonial rulers were very proud of the introduction of the "rule of law" in India. The evolution of criminal justice in colonial India as a form of "governmentality" is discussed at length in the book (pp. 87–107). These legal reforms facilitated the growth of forensic science in India. Starting from the mid-nineteenth century, institutions were established in different parts of India to ensure scientific certainty in criminal investigations and subsequent trials at courts. It began with the use of mug shots, police photographs of detained persons. Then came the Chemical Examiners' Laboratories, Anthropometric Bureau, Finger Print Bureau, Explosive Department, Handwriting Department, and several others (ch. 4). Chapter 5 discusses fingerprinting as a unique case study of forensic practice. William Herschel, a British civil servant in India, was the first to advocate the use of fingerprinting in the identification of criminal suspects. Very soon this technique received official recognition. This led to the establishment of the world's first-ever fingerprint agency, in 1897 in Calcutta. Here Edward Richard Henry, the inspector-general of police, Lower Bengal, with the able assistance of two Indian sub-inspectors—namely, Azizul Haque and Hem Chandra Bose—developed a system of classification of fingerprints that is well documented by the author. This book is a novel attempt to explore the colonial origin of forensic science in India along with the role played by nonmedical...

Changes in the own group bias across immediate and delayed recognition tasks

Face recognition is biased in favour of in-group identity, particularly strongly for race or ethnicity but to some extent also for sex and age. This ‘own group bias’ (OGB) can have profound implications in practical settings, with incorrect identification of black suspects by white witnesses constituting 40% of criminal exonerations investigated by the Innocence Project. Although authors have offered several explanations for the OGB in face recognition, there is little consensus, apart from the acknowledgement that the bias must reflect perceptual learning history. One matter that is not currently clear is whether the bias occurs at encoding, or at retrieval from memory. We report an experiment designed to tease out bias at encoding, versus bias at retrieval. Black and white South African participants encoded 16 target faces of both the same and other race and gender, and attempted immediately afterward to match the target faces to members of photograph arrays that either contained or did not contain the targets. After a further delay, they attempted to identify the faces they had encoded from memory. Results showed a strong crossover OGB in the delayed matching task, but an asymmetrical OGB at retrieval (only white participants showed the OGB). Further investigation of recognition performance, considering only images correctly matched in the delayed matching task, showed a narrowly non-significant OGB at retrieval, but the investigation was likely not sufficiently powered to discover the effect, if it exists. Significance: We demonstrate the presence of a crossover OGB in face recognition in a sample of black and white South Africans in a delayed matching task (a measure of encoding). Our findings show that the OGB may change rapidly. In the present study, the OGB took a crossover form at retrieval immediately after encoding, but was asymmetrical when assessed shortly afterwards. We used a novel approach for disentangling effects at encoding and at retrieval, but do not provide clear evidence to distinguish whether the OGB is a failure of encoding or of memory retrieval.

A Background of Bias: Subtle Changes in Lineup Backgrounds Increase the Own Race Bias

AbstractIn police photo lineups, there can sometimes be small variations in shades and hues of the background images due to the faces being filmed under different lighting and cameras. Own race bias refers to a situation where people are better at remembering the faces of those who are the same race as them and find it more difficult to recognise faces from a different race. In this paper, we investigated the influence of small colour variations in backgrounds for the recognition of Black and White faces. Across 3 experiments, we found when small changes were introduced into the backgrounds of the images this increased false identifications for previously unseen Black faces, but not White faces. This finding suggests that the police need to ensure that the backgrounds of the photo lineups they use are all uniform to reduce mistaken identifications of innocent suspects.

Potential causes of racial disparities in wrongful convictions based on mistaken identifications: Own-race bias and differences in evidence-based suspicion.

We explored whether racial disparities in evidence-based suspicion (i.e., evidence of guilt prior to placement in a lineup) provide a better explanation of racial disparities in exonerations based on eyewitness misidentification than the own-race bias in eyewitness identifications. We predicted that the own-race bias in identification accuracy would be insufficiently large to fully explain the racial disparities in wrongful convictions in cases with mistaken identification. We also predicted that possible racial disparities in the prior probability of suspect guilt before subjecting suspects to the risk of misidentification might better explain racial disparities in wrongful convictions. We conducted a meta-analysis on 54 effect sizes extracted from 16 studies (1,503 individual participants) that tested whether there was an own-race bias in eyewitness identifications using a design that varied the race of both the witnesses and the target faces (Black vs. White). We also constructed two curves that plotted the prior probability of suspect guilt against the posterior probability of guilt: one if an identification were to be obtained for a Black suspect and one if an identification were to be obtained for a White suspect. Participants, irrespective of their race, were better able to discriminate among previously seen White than Black targets. However, the differential accuracy rates for identifications of White versus Black suspects were too small to explain racial disparities in exoneration data on their own. However, racial disparities in evidence that police have against suspects before placing them in an identification procedure would likely explain more of the variance in racial disparities in mistaken identifications that lead to wrongful convictions. Memory errors caused by the own-race bias are likely not the sole or even primary cause of racial disparities in misidentifications; rather, systemic bias in the amount of evidence that police have before placing a suspect at risk of misidentification likely explains more of the variance of racial disparities in wrongful convictions based on mistaken identifications. Requirements for evidence-based suspicion before placing suspects in an identification procedure are needed to prevent systemic racism in mistaken identifications. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Latent Fingerprint Recognition using Hybrid Ant Colony Optimization and Cuckoo Search

Latent fingerprints are adapted as prominent evidence for the identification of crime suspects from ages. The unavailability of complete minutiae information, poor quality of impressions, and overlapping of multi-impressions make the latent fingerprint recognition process a challenging task. Although the contributions in the field are efficient for determining the match, there is a requirement to ameliorate the existing techniques as false identification can put the benign behind bars. This research work has amalgamated the Cuckoo Search (CS) algorithm with Ant Colony Optimization (ACO) for the recognition of latent fingerprints. It reduces the demerits of the individual cuckoo search algorithm, such as the probability of falling into local optima, the inefficient creation of nests at the boundary due to random walk and Levy flight attributes. The positive feedback mechanism of ant colony optimization makes it easy to combine with other techniques, reducing the risk of local failure and evaluating the global best solution. Prior to the evaluation of the proposed amalgamated technique on the latent fingerprint dataset of NIST SD-27, it is tested with the benchmark functions for different shapes and physical attributes. The benchmark testing and latent fingerprint evaluation result in the betterment of the amalgamated technique over the individual cuckoo search algorithm. The state-of-the-art comparison indicates that the amalgamation technique outperformed the other fingerprint matching techniques.

Deep Learning Based Face Detection and Identification of Criminal Suspects

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed. After detection and extraction of the face in the image by face cropping, the captured face is then compared with the images in the Criminal Database. The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric. After plotting the values in a graph to find the threshold value, we conclude that the confidence rate of the encoder model is 0.75 and above.

Rights Against Media Parade and Public Identification of Criminal Suspects: the Privacy Rage

Rights Against Media Parade and Public Identification of Criminal Suspects: the Privacy Rage

Stature Estimation from Dimension of Hands and Hand Prints in Dehradun Population

Background: Stature estimation by hand dimension is one of the common parameters in anthropometry but using the handprints for the same and its positive correlation with height of the subject, makes it a more useful parameter for identification of suspects who have left hand prints at the crime scene.[1]. Methodology: A cross-sectional study was carried out on 150 (75 males and 75 females) healthy subjects after taking voluntary informed consent. Stature was measured by stadiometer and hand and hand print dimension were measured by vernier caliper. Data was analysed by Statistical Package for Social Sciences (SPSS) version 20. Results: All parameter’s measurement were higher in male as compared to female subjects and shown range of weak to strong but positive correlation with height. Maximum positive correlation was observed in total population whereas minimum positive correlation was observed in between left hand breadth and left hand print breadth with height in female subjects. All the parameters were shown statistically significant correlation with height as p value was less than 0.05. Conclusion: The regression models thus formed is able to predict stature would be beneficial in challenges of identification of humans.

Failing at Face Value: The Effect of Biased Facial Recognition Technology on Racial Discrimination in Criminal Justice

Recent years have seen a rise in the development of technological innovations and their implementation in various industries. Specifically, law enforcement agencies across the United States have partnered with technology companies to deploy facial recognition algorithms in the identification and prosecution of criminal suspects. Yet there is concern that law enforcement’s use of facial recognition algorithms based on biased mugshot data pools can lead to criminalizing innocent civilians. Prominent theories including intersection theory, instrumentalization theory, and Alvarado’s theory were analyzed to review arguments that justify concern. We find that intersection theory is supported by empirical evidence that women of color are put at the greatest disadvantage from technological bias; instrumentalization theory is supported by examples of both positive and negative implementations of facial recognition technology, and Alvarado’s theory further suggests the possible reinforcement of existing biases by these poor applications of technology.