Multivariate Analytic Technique for Forensic Human Identification based on Dual Cross Patterns of Hand Radiographs
Sagar V. Joshi1, Rajendra D. Kanphade2
1Sagar V. Joshi*, Department of Electronics & Telecommunication, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India.
2Rajendra D. Kanphade, Department of Electronics & Telecommunication, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 2546-2552 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8629019320/2020©BEIESP | DOI: 10.35940/ijitee.C8629.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In recent times, the necessity for personal identification systems has increased due to several accidents. Under circumstances of human-made and natural disasters, it is not possible to employ a traditional biometric system. Hence, biometric radiographs of the skull, hands, and teeth are good replacement methods to identify victims. The fundamental intent of the research is to acquire a novel approach for identifying missing and anonymous individuals based on Dual Cross Pattern (DCP) features of hand radiographs. The suggested technique has contains two major steps: feature extraction and classification of the feature vectors. In this paper, an effort is made to find the most adequate classifier between the Classification Tree (CT), Feed forward Neural Network (FNN), Multiclass Support Vector Machine (m-SVM), and k-Nearest Neighbor (k-NN) based on the accuracy of retrieval of 10 adult subjects from the dataset of 300 right-hand radiographs. The classification results attained from simulation and discriminant analysis on a small primary database are encouraging.
Keywords: Discriminant Analysis, Dual Cross Grouping, hand Radiographs, Pattern Encoding
Scope of the Article: Pattern Recognition