Discriminating sex in South African blacks using patella dimensions

For many years, sex determination has been carried out on skeletal remains to identify individuals in forensic cases and to assess populations in archaeological cases. Since it has been shown that not all bones are found in a forensic case, discriminant function equations should be derived for all bones of the body to assist in sex determination. Numerous studies have shown the usefulness of bones of the lower extremity (e.g. femur, tibia) in sex determination using discriminant function analysis, but the use of patella measurements has not been extensively investigated for this purpose. It is therefore the aim of this study to derive discriminant function equations for sex determination from measurements of the patella of South African blacks as represented in the Raymond A. Dart Collection of Human Skeletons. A total sample of 120 (60 male, 60 female) patellae were measured using six measurements. The Statistical Product and Service Solutions (SPSS) program was used to derive the equations. Stepwise and direct analyses were performed with the highest rate of classification of 85% thereby making the patella useful for sex determination. Thus, the proposed equations derived from this study should be used with caution and only on the South African black population group.     

Sex Determination by Discriminant Function Analysis of Lumbar Vertebrae

Sex determination is critical for developing the biological profile of unidentified skeletal remains. When more commonly used elements (os coxa, cranium) for sexing are not available, methods utilizing other skeletal elements are needed. This study aims to assess the degree of sexual dimorphism of the lumbar vertebrae and develop discriminant functions for sex determination from them, using a sample of South African blacks from the Raymond A. Dart Collection (47 males, 51 females). Eleven variables at each lumbar level were subjected to univariate and multivariate discriminant function analyses. Univariate equations produced classification rates ranging from 57.7% to 83.5%, with the highest accuracies associated with dimensions of the vertebral body. Multivariate stepwise analysis generated classification rates ranging from 75.9% to 88.7%. These results are comparable to other methods for sexing the skeleton and indicate that measures of the lumbar vertebrae can be used as an effective tool for sex determination.     

Sexual dimorphism of the scapula and the clavicle in a contemporary Greek population: applications in forensic identification

Sex estimation is the grounds for an accurate identification of unknown human skeletal elements. The need for reliable methods distinguishing males from females based upon various skeletal elements is evident in cases of commingled, eroded and/or missing remains. The aim of this work lays on establishing criteria for sex estimation from the scapula and the clavicle in modern Greeks. A total of 147 left scapulae and 147 clavicles (66 females and 81 males) were used in the study. Eight and six measurements were taken on the scapula and clavicle respectively and data were subjected to principal components analysis (PCA) and discriminant function analysis (DFA). Posterior probabilities for the classification of each individual are also calculated. Statistical analysis was carried out using the software PAST (Paleontological Statistics) and SPSS (Statistical Package for Social Sciences) 18. The results supported the existence of pronounced sexual dimorphism, which was mainly attributed to size differences among the two groups. Univariate and multivariate methods of statistical classification showed high accuracy for all scapular and most clavicular measurements, verifying their value as sex indicators in the under study population. We recommend the use of this method for sex assessment from the scapula and the clavicle in cases exhibiting over 95% probability of correct classification. This is regardless of the overall high degree of accuracy reported here, as the method of choice in forensic contexts should always be case-driven.     

Hair-MAP: a prototype automated system for forensic hair comparison and analysis

This paper demonstrates the feasibility of the automation of forensic hair analysis and comparison task using neural network explanation systems (NNESs). Our system takes as input microscopic images of two hairs and produces a classification decision as to whether or not the hairs came from the same person. Hair images were captured using a NEXTDimension video board in a NEXTDimension color turbo computer, connected to a video camera. Image processing was done on an SGI indigo workstation. Each image is segmented into a number of pieces appropriate for classification of different features. A variety of image processing techniques are used to enhance this information. Use of wavelet analysis and the Haralick texture algorithm to pre-process data has allowed us to compress large amounts of data into smaller, yet representative data. Neural networks are then used for feature classification. Finally, statistical tests determine the degree of match between the resulting collection of hair feature vectors. An important issue in automation of any task in criminal investigations is the reliability and understandability of the resulting system. To address this concern, we have developed methods to facilitate explanation of neural network's behavior using a decision tree. The system was able to achieve a performance of 83% hair match accuracy, using 5 of the 21 morphological characteristics used by experts. This shows promise for the usefulness of a fuller scale system. While an automated system would not replace the expert, it would make the task easier by providing a means for pre-processing the large amount of data with which the expert must contend.     

Estimating Sex Using Metric Analysis of the Scapula by Postmortem Computed Tomography

Postmortem computed tomography (CT) has been extensively used in the last decade for identification purposes and in various anthropologic studies. Postmortem CT measurements of scapulae, analyzed using logistic discriminant function developed in this study, showed 94.5% accuracy in estimating sex. Data analyzed using the Dabbs and Moore‐Jansen (2010) discriminant function and the discriminant function generated in this study provided nearly identical results with disagreement in only one case. Height and weight were not statically significant in sex prediction. The results of this study show that data obtained from volume rendered postmortem CT images can be considered reliable and treated as a practical option to standard anthropological methods, especially in mass fatalities as a rapid triage tool for sex determination.     

Improving Sex Estimation from Crania Using a Novel Three‐dimensional Quantitative Method,,

This study utilizes an innovative 3D approach to discover metric variables that obtain the highest classification rates for sex estimation from the cranium. Models were constructed from 222 cranial CT scans of U.S. Whites from the Bass Donated Collection. These models were used to create a statistical bone atlas that captures the primary shape variation in the skull and facilitates rapid computer‐automated analyses. The bone atlas showed that important size‐related sex variables are bizygomatic breadth, maximum cranial length, cranial base length, and mastoid height. Shape‐related variables capture sex differences in the projection of the glabellar region, inclination of the frontal, and cranial base flexion. In addition, vault thickness is highly dimorphic, with females having on average thicker vaults in the frontal region, and males having thicker vaults in the occipital region. Cross‐validated linear discriminant analysis obtained >95% accuracy (97.5% with 11 variables and 95.5% with eight variables).     

Evaluation of maxillary sinus dimensions in gender determination using helical CT scanning

Gender determination is an important step in identification in forensic medicine. CT measurements of maxillary sinuses may be useful to support gender identification. This study was undertaken to study the accuracy and reliability of maxillary sinus dimensions measurement in gender classification through the use of reconstructed helical CT images. Eighty-eight patients (43 men and 45 women) with age range from 20 to 49 years were selected in this study. The width, length, and height of the maxillary sinuses in addition to the total distance across both sinuses were measured. Data were subjected to discriminant analysis for gender using multiple regression analysis. Maxillary sinus height was the best discriminant parameter that could be used to study sexual dimorphism with an overall accuracy of 71.6%. Using multivariate analysis, 74.4% of male sinuses and 73.3% of female sinuses were sexed correctly. The overall percentage for sexing maxillary sinuses correctly was 73.9%. It can be concluded that reconstructed CT image can provide valuable measurements for maxillary sinuses and could be used for sexing when other methods of sexing are not conclusive.     

Identification of sex depending on radiological examination of foot and patella

Forensic anthropology involves the building of an antemortem profile of an individual from skeletal remains. This includes sex, race determination, and age and stature estimation. Because most bones that are conventionally used for sex determination are often recovered either in a fragmented or incomplete state, it has become necessary to use denser bones that are often recovered intact, eg, the patella, calcaneus, and talus. The present work was performed to investigate the possibility of estimation of sex from some radiologic measurements among a known cross-section of Egyptian population. In this study lateral and anteroposterior radiographs of the right foot and knee were made on 160 living unfractured and nonpathologic individuals comprising 80 males and 80 females aged 25 to 65 years referred to the Radiology Department of Assiut University Hospital. Two measurements on right patella (maximum height and maximum width) and 2 measurements of metatarsal bones (length and midshaft diameter), were used to determine sex by univariate and multivariate discriminant analysis. Eighty radiographs of foot and patella of individuals not used in the original sample were randomly selected to test the accuracy of this method. The study revealed that significant sex differences were demonstrated based on these measurements taken on metatarsal bones more than on patella. One function associating 2 parameters (length and midshaft) of the third metatarsal bone obtained the highest value of correct sex determination with rate of 100% accuracy. The multivariate function associating length of the first, third, and fifth metatarsal bones and midshaft of first, second, and fifth metatarsal gave 100% accuracy. Test of multivariate function on the independent sample revealed a correct classification of 87.5%.     

Testing the validity of population-specific sex estimation equations: An evaluation based on talus and patella measurements

Sex estimation is essential for forensic scientists to identify human skeletal remains. However, the most sexually dimorphic elements like pelvis or skull are not always assessable. Osteometric analyses have proven useful in sex estimation, but also to be population specific. The main purpose of this study was to test the validity of contemporary Greek and Spanish discriminant functions for the talus and the patella, respectively, on a Swiss skeletal sample and to quantify the utility of the measurements as a novel approach in osteometric sex assessment.Four talus and three patella measurements on dry bone were obtained from 234 individuals of the modern cemetery SIMON Identified Skeletal Collection. The previously derived discriminant functions were applied, accuracies determined, the utility of the different measurements was assessed and new multivariable equations constructed.Accuracies varied between 67% and 86% for talus and 63% and 84% for patella, similar to those reported by the original studies. Multivariable equations should be preferred over equations based on single measurements and combining the most significant measurements rather than using several variables obtained the best possible accuracy. The new discriminant functions did not provide a substantial improvement to the original ones. The overall utility of talus and patella is limited, allowing sex estimation with sufficient certainty only in a small proportion of individuals.Discriminant functions developed in contemporary Greek or Spanish populations are in principle applicable also to Swiss contemporary populations. We recommend that at present existent studies of this type should be validated and tested rather than developing new formulas.     

A comparison between neural network and other metric methods to determine sex from the upper femur in a modern French population

Forensic anthropologists are frequently asked to assess partial or badly damaged skeletal remains.One such request led us to compare the predictive accuracy of different mathematical methods using four non-standard measurements of the proximal femur (trochanter–diaphysis distance (TD), greater–lesser trochanter distance (TT), greater trochanter width (TW) and trochanter–head distance (TH)). These measurements were taken on 76 femurs (38 males and 38 females) of French individuals. Intra- and inter-observer trials did not reveal any significant statistical differences. The predictive accuracy of three models built using linear and non-linear modelling techniques was compared: discriminant analysis, logistic regression and neural network. The neural network outperformed discriminant analysis and, to a lesser extent, logistic regression. Indeed, the best results were obtained with a neural network that correctly classified 93.4% of femurs, with similar results in males (92.1%) and females (94.7%). Univariate functions were less accurate (68–88%). Discriminant analysis and logistic regression, both using all four variables, led to slightly better results (88.2% and 89.5%, respectively). In addition, all the models, save the neural network, led to unbalanced results between males and females. In conclusion, the artificial neural network is a powerful classification technique that may improve the accuracy rate of sex determination models for skeletal remains.     

The classification and discrimination of glass fragments using non destructive energy dispersive X-ray microfluorescence

Frequency of analytical characteristics is best estimated on glass recovered at random. However, as such data were not available to us, we decided to use control windows for this estimation. In order to use such a database, one has to establish that the recovered fragment comes from a window. Therefore, elemental analysis was used both for classification and discrimination of glass fragments. Several articles have been published on the subject, but most methods alter the glass sample. The use of non destructive energy dispersive X-ray microfluorescence (microXRF) for the analysis of small glass fragments has been evaluated in this context. The refractive index (RI) has also been measured in order to evaluate the complementarity of techniques. Classification of fragments has been achieved using Fisher's linear discriminant analysis (LDA) and neural networks (NN). Discrimination was based on Hotelling's T2 test. Only pairs that were not differentiated by RI followed by the Welch test were studied. The results show that neural network and linear discriminant analysis using qualitative and semi-quantitative data establishes a classification of glass specimens with a high degree of reliability. For discrimination, 119 windows collected from crime scene were compared: using RI it was possible to distinguish 6892 pairs. Out of 129 remaining pairs, 112 were distinguished by microXRF.     

Automated recovery of damaged audio files using deep neural networks

In this paper, we propose two methods to recover damaged audio files using deep neural networks. The presented audio file recovery methods differ from the conventional file carving-based recovery method because the former restore lost data, which are difficult to recover with the latter method. This research suggests that recovery tasks, which are essential yet very difficult or very time consuming, can be automated with the proposed recovery methods using deep neural networks. We apply feed-forward and Long Short Term Memory neural networks for the tasks. The experimental results show that deep neural networks can distinguish speech signals from non-speech signals, and can also identify the encoding methods of the audio files at the level of bits. This leads to successful recovery of the damaged audio files, which are otherwise difficult to recover using the conventional file-carving-based methods.     

Ancestry Estimation Using Cranial and Postcranial Macromorphoscopic Traits

Ancestry estimation methods using macromorphoscopic (MMS) traits commonly focus exclusively on cranial morphology. The objective of this study was to demonstrate the value of postcranial MMS traits, highlighting a combined cranial/postcranial trait approach to ancestry estimation using quadratic discriminant function and a variety of machine learning classification models including artificial neural networks (aNN), random forest models, and support vector machine. Eight cranial and eleven postcranial MMS traits were collected from the Terry and Bass Skeletal Collections (American Black = 81; American White = 173). Our classification models using cranial and postcranial traits correctly classified 88–92% of the sample, improving classification accuracies by nearly fifteen percent over models relying exclusively on cranial data. These same results demonstrate the importance of a multivariate statistical framework incorporating cranial and postcranial data and the nearly unlimited potential of machine learning models to improve the accuracy of ancestry estimates over traditional methods of analysis. To facilitate implementation in casework, one of the more robust models (aNN) is incorporated into a web-based application, ComboMaMD Analytical, to facilitate cranial and postcranial MMS traits analysis for ancestry estimation.     

Sexual Dimorphism in Ulna: An Osteometric Study from India

Determination of sex constitutes the most important element during the identification process of human skeletal remains. Several sex‐specific features of human skeleton have been exploited for sex determination with varying reliability. This study aims to obtain sexual dimorphic standards for ulnae of the north Indian population. Eight measurements were obtained on a sample of 106 ulnae (males‐80, females‐26) in the age range of 25–65 years. The sexual dimorphism index and demarking points were calculated for all the variables. The data were then subjected to stepwise and direct discriminant function analysis. The best discriminator of sex was the maximum length (84.9%) followed by radial notch width (84%). In stepwise analysis, these two variables were selected and provided an accuracy of 88.7% (M‐87.5%, F‐92.3%). The proximal end provided a classification rate of 81.1% (M‐80%, F‐84.6%) with selection of the notch length and olecranon width.     

Univariate sex dimorphism in the Nepalese dentition and the use of discriminant functions in gender assessment

Sex dimorphism in the Nepalese dentition is described using univariate and discriminant analyses. Canines showed the greatest univariate sex dimorphism, followed by the buccolingual (BL) dimension of maxillary first and second molars. Overall, the maxillary teeth and BL dimensions showed greater univariate sex differences. However, less than half of the measured variables (46.4%) showed statistically significant differences between the sexes and the magnitude of sex dimorphism was reduced when compared to other populations. Moreover, reverse dimorphism--where females showed larger teeth than males--was observed in the mesiodistal dimension of mandibular second premolars. This reflects reduction in sexual dimorphism observed through human evolution and the consequent overlap of tooth dimensions in modern males and females. A specific purpose of the study was to develop discriminant functions to facilitate sex classification. A group of functions were developed considering the possibility of missing teeth and/or jaws in forensic scenarios. The functions permitted moderate to high classification accuracy in sexing (67.9% using maxillary posterior teeth; 92.5% using teeth from both jaws). The superior expression of sex dimorphism by means of discriminant functions is in contrast to the univariate results. This is due to discriminant analysis utilising the inter-relationship between all teeth within a dentition--these tooth correlations are not utilised in univariate analysis which results in a loss of information. It is inferred that large-scale statistically significant univariate differences are not a prerequisite for sex assessment.     

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.     

Development and Assessment of Postcranial Sex Estimation Methods for a Guatemalan Population

This study tests whether postcranial sex estimation methods generated from Hispanic, and mainly Mexican samples, can be successfully applied to other increasingly common migrant populations from Central America. We use a sample of postcranial data from a modern (1980s) Guatemalan Maya sample (n = 219). Results indicate a decrease in classification accuracies for previously established univariate methods when applied to the Guatemalan study sample, specifically for males whose accuracies ranged from 30 to 84%. This bias toward inaccuracies for Guatemalan males is associated with the smaller skeletal sizes for the Guatemalan sample as compared to the samples used in the tested sex estimation methods. In contrast, the tested multivariate discriminant function classification yielded less sex bias and improved classification accuracies ranging from 82 to 89%. Our results highlight which of the tested univariate and multivariate methods reach acceptable levels for accuracy for sex estimation of cases where the region of origin may include Guatemala.     

Testing the validity of metacarpal use in sex assessment of human skeletal remains

To assess the potential of employing metacarpals in assessing sex of human skeletal remains, previous investigators have generated regression equations (Scheuer & Elkington, 1995) and linear discriminant functions (Falsetti, 1995; Stojanowski, 1999) based upon measurements from metacarpals. Results have varied in overall accuracy and which metacarpal produces the greatest accuracy. Using a contemporary sample, this study seeks to evaluate the validity of using metacarpals to assign sex by testing methodologies of previous studies. Measurements defined by previous authors were repeated on metacarpals from 23 adult cadavers and data were subjected to regression equations and linear discriminant analysis according to previous methodologies. Accuracy in sex determination from methods of Scheuer & Elkington (1993) and Falsetti (1995) were lower than originally reported while accuracy from methods of Stojanowski (1999) were higher than previously reported. These results suggest that the use of metacarpals in sex determination may be limited and should be applied cautiously.     

Metric determination of sex from the humerus in a Guatemalan forensic sample

The absence of population-specific standards for sex, age and stature estimation for rural Guatemala is problematic for the forensic analysis of skeletal remains recovered from clandestine graves attributed to the recent armed conflict in that country. In order to increase the reliability of the forensic analyses being undertaken in Guatemala, standards for metric determination of sex were developed. Data was collected on several bones; the results for the humerus are presented here. A sample of 118 complete humeri (68 male and 50 female) was studied; maximum length, maximum diameter of the head, circumference at midshaft, maximum diameter at midshaft, minimum diameter at midshaft and epicondylar breadth were measured and subjected to discriminant function analysis. The classification accuracies for the univariate functions range from 76.8% for the maximum diameter at midshaft to 95.5% for the maximum diameter of the head. The classification accuracy for the stepwise procedure was 98.2%.