Sex determination is first step for identification followed by age and stature estimation as both are sex dependent To identify sex in explosions, mass disaster, air hurricanes from decayed and...

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Sex determination is first step for identification followed by age and stature estimation as both are sex dependent To identify sex in explosions, mass disaster, air hurricanes from decayed and damaged dead bodies (non living) is the most challenging task for forensic experts.It has been carried out by many methods, morphological assessment was considered as oldest approach in forensic odontology and medico-legal cases. Depending upon the available bones and their condition the methods varies for determination of sex. The pelvis and skull are the most reliable source among human bones. Mandible becomes important source for sex confirmation in absence of complete pelvis as mandible is considered as most durable facial bone that retains its shape better than others. Mandible is the largest, strongest and movable part of the skull. They are extremely durable in fire and bacterial decomposition makes them invaluable for identification. The mandibular ramus is quadrilateral, and has two surfaces, four borders and two processes. The lateral surface is relatively featureless. Radiography is commonly accessible, less invasive and is used in routine procedures. Panoramic radiography is widely used method for obtaining a comprehensive overview of the maxillofacial complex. Principal component analysis (PCA) is the most fundamental, general purpose multivariate data analysis method used in chemometrics. A geometrical projection analogy is used to introduce derivation of bilinear data models, focusing on scores, loadings, residuals, and data rank reduction. This is followed by a presentation and comparison of three alternative algebraic formulations for components analysis as well as algorithms for their calculations. Based on the data distribution in high dimensional space, PCA analysis extracts a series of principal components (linear transformed coordinates), where data in the first principal component has the largest variant. The second principal component is perpendicular (orthogonal) to the first principal component and has the second largest variant. The underlying assumption is that the coordinates with the large variants most saliently demonstrate the contrast between sample points, while the coordinates with smaller variants may be a source of noise, which should be ignored or suppressed. In the meantime, the correlation between two dimensions represents redundant information, which will not be presented. That is why this algorithm requires the following coordinates to be perpendicular (orthogonal) to previous coordinates. A PCA analysis can reduce the dataset of m dimension into n dimension, n<= m, by selecting the first n principal components by="" selecting="" the="" first="" n="" principal="">