34 Computer Applications

Introduction

Forensic anthropologists rely on computers to evaluate information effectively and to accelerate the lengthy and complex process of collecting and analysing evidences. They use computer software program with algorithms to calculate traces of data and to find possible matches. In this manner, forensic anthropologists are able to conclude the depiction of a crime, how it occurred and who and what was implicated in the crime, often solving a case, so that justice is served.

Computational methods find a place in the forensic science in three ways. First, they provide tools for the human examiner to better analyze evidence by overcoming limitations of human cognitive ability- thus they can support the forensic examiner in his/her daily casework. Secondly they can be used to provide the scientific basis for a forensic discipline or procedure by providing for the analysis of large volumes of data which are not humanly possible. Thirdly they can be ultimately be used to represent human expert knowledge and for implementing recognition  and reasoning  abilities in machines. (Franke and Srihari, 2008)

More broadly, the computer applications enable forensic anthropologists or forensic scientists to:

• Reveal and Improve trace evidences for future investigation
• Analyze and identify evidence in an objective and reproducible manner
• Assess the quality of the examination method
• Report and standardize investigative procedures
• Search large volumes of data efficiently
• Visualize and document the result of analysis
• Assist in the interpretation of results and their argumentation
• Reveal previously unknown pattern/links, to derive new rules and contribute to the generation of new knowledge

Computational Forensics:

Computational forensics is a new interdisciplinary research domain. Computer forensics is the scientific examination and analysis of data held on, or retrieved from, computer storage media in such a way that the information can be used as evidence in a court of law.

It is understood as the hypothesis-driven investigation of a specific forensic problem using computers, with the primary goal of discovery and advancement of forensic knowledge.

In addition, research and development in the computing sciences can profit from problem definitions and work procedures applied in forensics, e.g.,

• Forensic data, skilled forgeries and partial, noisy data that pose challenging problems regarding the robustness of an automatic system.
• Computer scientists can gain new insights in analysis procedures while taking the perspective of a forensic expert who has expertise in his / her field of specialization.
• Computational  approaches  undergo  fine  tuning  to  achieve  superiority,  but  eventually  also generalization. (Franke and Srihari, 2008)

Forensic methods can be assisted by algorithms and software from several areas in the computational science. Some of these are:

• Signal  /  image  processing:  where  one-dimensional  signals  and  two-dimensional  images  are transformed for the purpose of better human or machine processing,
• Computer vision: where images are automatically recognized to identify objects,
• Computer  graphics  /  data  visualization:  where  two-dimensional  images  or  three-dimensional scenes are synthesized from multi-dimensional data for better human understanding,
• Statistical pattern recognition: where abstract measurements are classified as belonging to one or more classes, e.g., whether a sample belongs to a known class and with what probability,
• Data mining: where large volumes of data are processed to discover nuggets of information, e.g., presence of associations, number of clusters, outliers in a cluster,
• Robotics: where human movements are replicated by a machine, and
• Machine learning: where a mathematical model is learnt from examples.(Franke and Srihari, 2008)

The relevant areas of computational applications in the field of forensic anthropology are as follows:

1.   Facial Reconstruction:

Forensic facial reconstruction is the method of reconstructing the living face of an individual from skeletal remains to aid in identification. In recent years computer technology has been used in facial reconstruction which allows better manipulation of the image and easy transfer between computers. The skull is rotated whilst a laser scanner is used to produce a 3D image of the skull. Previously- obtained computed tomography scans of actual living people are used to determine the muscle, fat and skin to be placed over the skull, the profiles used are selected based on their similarity to the victim. These methods are often preferred as they are non-destructive to the skull, so if errors are made, the process can be repeated without altering the evidence. (Rankin, 2005)

Phillips and Smuts (1996) studied facial reconstruction using computerized tomography to measure facial tissue thickness in a mixed racial population. They found out that the use of the computerized tomography scanning procedure to measure soft tissue depth is more accurate than the needle probe technique. The results obtained from this study are therefore representative of the variation in facial tissue thickness of South Africans of mixed racial origin for facial reconstruction purposes.

Vanezis et al., (2000) used facial reconstruction software which was constructed using the TCL/Tk scripting language, the latter making use of the C3D® system. In this technique, the computer image may then be exported to enable the production of a solid model, employing, for example, stereo lithography. The image can also be modified within an identitykit system which also allowed the addition of facial features as appropriate.

Examples of facial reconstruction using 3D computer graphics includes the identification of a murder victim, reconstruction of historical/archaeological skulls and mass grave identification.

2.   Fingerprint Recognition:

The practice of using finger prints as personal identification has been in use since late nineteenth century when Sir Francis Galton defined some of the points or characteristics from which fingerprints can be identified. With the advent of computers, a subset of the Galton points, referred to as minutiae, has been utilized to develop automated fingerprint technology. Pattern matching is also used in the finger print recognition technology. It simply compares two images to see how similar they are. Pattern matching is usually used in fingerprint systems to detect duplicates. The most widely used recognition technique, minutiae-based matching, relies on the minutiae points described above, specifically the location and direction of each point. (Maltoni et al, 2009)

Fingerprint recognition technology is extensively used in the personal identification, network security, cellular phones, personal digital assistants, car control and security, home control and security.

3.   Handwriting Recognition:

Handwriting recognition is the process of transforming a handwriting represented in its spatial form of graphical form into its symbolic representation with the help of software and algorithm. There are two approaches:  offline  and  online  handwriting  recognition.  In  the  offline  case,  only the  completed handwriting is available as an image whereas in online case, the order of the strokes made by the writer is readily available. The process of handwriting recognition can be broken down into three general steps: Pre-processing, Feature Extraction and Classification. In pre-processing irrelevant information in the input data is discarded and it usually consists of binarization, normalization, sampling, smoothing and denoising. The purpose of feature extraction step is to highlight important information for the recognition model. In classification step various models are used to map the extracted features to different classes and thus identifying the characters or words the features represent.

Handwriting recognition offers a wide variety of applications. The most important of these has been in reading postal addresses, bank check amounts and forms.

3.1 Handwritten address interpretation

A handwritten address interpretation system (HWAI) is one of the major applications of handwriting recognition which is currently in use by United States Postal Services. Recognition process is based on the lexicon size of the words.

Bank Check Recognition

With the advancements in the field of handwriting recognition it is also possible to recognize the amounts written in the bank checks. It involves image filtering, binarization, segmentation of text blocks and removal of noise.

4.   Forensic Dentistry:

Forensic odontologists when obtain dental evidence in a homicide case it may be of bite marks on the victim, which may pinpoint the attacker, and teeth from the corpse. To identify the criminal or victim, they search through dental records and databases for a match. Scientists use digital imaging software to help expedite the process and ensure precision. The computerized system can measure accurately any parameters of evidence, correct any size of discrepancies and disregard any bias of opinion. (Mei, 2009)

Advances in computer technology coupled with improvement in sensor technology over the past 25 years have resulted in the adoption of digital radiography systems in dental practice. These now include orthopanographic, cephalographic, and computed tomographic images for nearly instant delivery to clinical workstations. This stands in marked contrast to film-based dental radiography, particularly in forensic applications, and holds many advantages for the forensic dentist. The dental investigator is able to speedily discern whether or not features that might serve to confirm or refute a suspected identification are visible and order re-imaging if needed while the specimen is available.

Digital radiographic imaging software also allows the investigator to optimize or enhance the image as viewed onscreen, resulting in visualization of greater detail in the captured image – again increasing the likelihood of an identification or exclusion. (Barsley, 2011)

5.   Speech Recognition:

Speech or voice recognition is the process in which the human voice is decoded into digitized speech with the help of computer software program. Automatic speech recognition require the user to “train” the Speech Recognition program to recognize their voice so that it can more accurately convert the speech to text. The voice recognition system is of different types such as text dependent, text independent, discrete and continuous types. The speech recognition system takes raw audio input data and translates it to recognized text that an application understands. For speech recognition, all sorts of data, statistics and algorithms are employed. The first step is to process the incoming audio signal and convert it into a format best suited for further analysis. Once the speech data is in the proper format, the engine searches for the best match. It does this by taking into consideration the words and phrases it knows about (the active grammars), along with its knowledge of the environment in which it is operating (for Voice XML, this is the telephony environment). The knowledge of the environment is provided in the form of an acoustic model. Once it identifies the most likely match for what was said, it returns what it recognized as a text string. (Kemble, 2007)

6.   Hand Geometry Recognition:

Hand geometry is a biometric technique which is used for identification. Hand geometry recognition uses computer applications for the hand geometry measurements. These geometric measurements include length and width of hand and hand shape. Hand biometric system consists of pre-processing, feature extraction and feature matching. Under pre-processing, binarization, edge detection, image enhancement is done through different computer algorithms. A number of feature vectors are extracted from the hand geometry in the feature extraction process which is followed by the matching stage based on the classification algorithm that generates a distance score for each template comparison using a feature vector’s similarity measure. The score which is having lowest distance value represents the best match. After matching algorithm a recognition decision is taken by computer software whether to accept or reject the best match found.

7.   Palm print recognition:

The palms of the human hands contain unique pattern of valley, ridges, principal line features and minutiae. Palmprint recognition techniques can be extracted into two categories: One that is based on the extraction of principal lines, wrinkles and textures and second is based on the extraction of ridges, minutiae and singular points. This recognition system also follows three steps: pre-processing, feature extraction and feature matching. A number of feature extraction algorithm has been used in the area of palm print recognition such as K-L transform (Lu et al, 2003), circular Gabor filter (Zhang et al, 2003), modified finite random transform (Huang et al, 2008).

8.   Ear Recognition:

Ear recognition in the field of computer science is done through the use of computer algorithms. Ear detection is the first step of human ear recognition system and its performance affects the quality of the system. Ear detection has two approaches: offline and online ear detection. In Offline approach side face image is acquired with the help of camera. After taking image segmentation of ear (ear detection) from input image, feature extraction from the ear region and then recognition is done. Burge and Burger (1996)developed a computer algorithm to recognize ears in the intensity images. This algorithm composed of four parts: edge extraction, curve extraction, construction of a graph model from the Voronoi diagram of the edge segments, and graph matching.

9.   Iris Recognition:

Iris recognition  is an automated method  of biometric identification that uses pattern recognition algorithm. In iris recognition, identification is carried out by taking images of eye with a high resolution digital camera at visible or infrared (IR) wavelengths. Important identifying features are then extracted from the acquired image. This image is then compared with other iris templates stored in the biometric database with the help of a computer program called matching engine. The matching engine can compare millions of images per second with a level of accuracy. Daugman (1993) developed the feature extraction process based on information from a set of 2-D Gabor filter.

10.   Retina Recognition:

The retina is a thin layer of cells that is a light sensitive layer of tissue, lining the inner surface of the eye. The overall retina scanning process can be broken down into three sub-processes: Image/signal acquisition/Pre-processing, Feature extraction and Matching. Image acquisition involves capturing of image and converting it to digital format. In this step an image enhancement technique is also employed for the compensation of non-uniform contrast and luminosity distribution in retinal images. After this unique features of retinal image are extracted and presented as a template through computer algorithm. This template is then used to verify and identify the user (as in the case of other recognition technologies).

11.   DNA Fingerprinting:

The power of DNA technology as an identification tool has brought a tremendous change in crime investigation. DNA sequencing is the succession of genetic codes in living cells, which are unique in every organism. The Combined DNA Index System (CODIS) blends computer and DNA technologies into an effective tool for comparing DNA profiles. The current version of CODIS uses two indices to generate investigative leads in crimes where biological evidence is recovered from the crime scene. The Convicted Offender index contains DNA profiles of individuals convicted of violent crimes, including sex offences. The Forensic Index contains DNA profiles developed from crime scene evidence. CODIS utilizes computer software to automatically search these indices for matching DNA profiles. (Panneerchelvamand Norazmi, 2003)

12.   Digital Blood Stain Analysis:

The examination of the shapes, locations and distribution patterns of blood stains give forensic examiner clues to physical events by which they were created. The use of computer program with trigonometric functions is very helpful to pinpoint the exact direction where each droplet landed and to infer from its size and path how much trauma was forced upon the victim, the kind of weapon used and the location of the victim or attacker during the incident. The tangential algorithm also helps to determine the point of convergence (intersection of two bloodstain paths, where the stains come from opposite sides of the impact pattern) and area of convergence (box formed by the intersection of several stains from opposite sides of the impact pattern).

13.   Computer Simulation of a Crime Scene:

With the help of computer technology it is possible to present a crime scene in 3-D imagery. Scientists use special computer software such as Crime Zone, witness or Sphero Cam HDR to visualize a re- enactment of a crime scene in a two or three-dimensional display. The software evaluates digital pictures and data taken from the site and simulates a 3D virtual reality of the event. Panoramic images are printed and presented on a screen before a court, allowing the jury to view the events of the crime. (Mei, 2009)

The impact of computer applications in the forensics is far reaching. The important contributions to forensic science domain are to:

• Increase efficiency and effectiveness in risk analysis, crime prevention, investigation, prosecution and the enforcement of law, and to support standardized reporting on investigation results and deductions.
• Perform testing that is often very time consuming. By means of systematic empirical testing scientific foundations can be established. Theories can be implemented and become testable on a larger scale of data. Subsequently, method can be analyzed regarding their strengths /weaknesses and a potential error rate can be determined.
• Gather, manage and extrapolate data, and to synthesize new data sets on demand. In forensics, unequally distributed data sets exist; there are many correct but only a few are counterfeit samples. Computer models can help to synthesize data and even simulate meaningful influences / variations.
• Establish and to  implement  standards  for  work procedures and to  journal processes (semi)- automatically. Technical equipment also supports the establishment /maintaining of conceptional frameworks and terminologies used. In consequence, data exchange and the interoperability of systems become feasible.(Franke and Srihari, 2008)

Conclusion:

The use of computing tools in the forensic discipline is very useful. Research and development in computational forensic has led many advancements in the field of forensic science. With the introduction of computer-based methods in the investigation process, new work procedures and legal frameworks take advantage of both knowledge domains; forensic and computational sciences. Computational forensics hold the potential to greatly benefit all forensic science disciplines. It poses a new frontier for the computer scientist where new problems and challenges are to be faced. The potential benefits to society, meaningful inter-disciplinary research, and challenging problems should attract high quality students and researchers to the field.