Person identification in Ethnic Indian Goans using ear biometrics and neural networks

Dinkar, Ajit D; Sambyal, Shruti S.

doi:10.1016/j.forsciint.2012.08.032

Cited by 19 publications

(10 citation statements)

References 9 publications

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“…The occurrence and frequencies with respect to these characteristics have been calculated, and population-based characteristics have been compiled to link these characteristics with a community. Some studies have metrically observed the structure of the ear (Ahmed and Omer 2015;Dinkar and Sambyal 2012;Sforza et al 2009;Purkait and Singh 2007;Gibelli et al 2012;Purkait and Singh 2008) such as linear distances, ratios, growth changes in the ear, and bilateral asymmetry of the ear and tried to formulate the population and community characteristics for anthropological and forensic use. Most of these studies have shown that the ear dimensions are metrically variable in individuals and populations, the ear parameters are significantly larger in males than females, and ears show bilateral asymmetry.…”

Section: Introductionmentioning

confidence: 99%

A study of morphological variations of the human ear for its applications in personal identification

Krishan

Kanchan

Thakur

2019

Egypt J Forensic Sci

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Background: External human ear is considered to be a highly variable structure showing different morphological and individualistic features in different individuals and population groups. The uniqueness of the ear may be useful in establishing the identity of individuals by direct examination, during the examination of CCTV footage or analysis of the ear prints. Considering the forensic significance of the human ear and ear prints encountered at the scene of the crime, the present study is an attempt to evaluate various morphological characteristics of the ear in a north Indian population. Methodology: The sample for the present study comprises of 90 males and 87 females aged between 18 and 30 years. All the study participants were from upper reaches of Himachal Pradesh in North India. The morphological characteristics such as overall shape of the ear, size and shape of the tragus, earlobe, shape of the helix, and forms of Darwin's tubercle were studied in the participants. Results: The oval-shaped ear was present among 40% of the males and 44.8% of the females in the study sample. The other types of the ear such as oblique, rectangular, round, and triangular were also found in both sexes. Bilateral asymmetry was observed in the shape of the ear. The shape of the tragus also varied with respect to the left and right sides as well as sexes. The earlobe showed different characteristics in different individuals. In nearly half of the cases in both males and females, the earlobe was found to be attached to the face; in many cases, it was free and in some partially attached. The size and shape of the earlobe also showed variations with respect to sides as well as sexes. The Darwin's tubercle showed a variety of structures in both the left and right sides in both sexes.Conclusion: The present study shows that the individualistic characteristics of the ear can provide very useful information for personal identification in forensic examinations. The shape of the ear and the important structures such as the tragus, helix, earlobe, and Darwin's tubercle show a variety of structures and individuality. The importance and variability of the human ear may encourage the researchers in conducting further studies and solving the forensic cases pertaining to the investigation of CCTV footage and in examination of dead in airplane crashes, intentional mutilation and dismemberment, explosions, or other mass disasters.

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Section: Introductionmentioning

confidence: 99%

A study of morphological variations of the human ear for its applications in personal identification

Krishan

Kanchan

Thakur

2019

Egypt J Forensic Sci

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“…It has been used as a major feature in forensic practice due to its fixed location on the lateral side of the face and because it has a size which is larger than other facial parts, e.g., the retina and iris [3]. Furthermore, it has shown relative stability of shape and orientation compared to facial expression.…”

Section: Introductionmentioning

confidence: 99%

Estimation of sex from the anthropometric ear measurements of a Sudanese population

Ahmed

Omer

2015

Legal Medicine

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“…The aforementioned scientific researches are next described in brief. Dinkar and Sambyal (2012) present new insights and experimental results for the use of ears as a non-invasive biometric for human identification. To determine the uniqueness of the external ear pattern two methods were employed: The Weighted Scoring System and Pattern Recognition by Neural networks.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 93%

“…These features were then converted to numeric scores by the 'Weighted Scoring System' which were then compared to ascertain the uniqueness of ear pattern in same and different individuals. The digital analysis of visually similar ear images by Neural networks revealed a recognition rate of 94 % with an Equal Error Rate at threshold value of 0.225 (Dinkar and Sambyal 2012). Sibai et al (2011) present a simple methodology for pre-processing iris images and the design and training of a feedforward artificial neural network for iris recognition.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…For example, artificial neural networks (i.e., systems that learn from data) have been used in different biometric applications involving pattern classification and identification (of a human (Dinkar andSambyal 2012, Melin et al 2012), of driver (Wu and Ye 2009), of finger-vein patterns (Wu and Liu 2011), of iris recognition (Sibai et al 2011), of human action (Youssef and Asari 2013), of gait (Zeng and Wang 2012), of the face (Connolly et al 2013;Kuo et al 2011;Choi et al 2012;Banerjee and Datta 2013;Lin and Lin 2013;Müller et al 2013), of the hand (Michael et al 2008), of the skin (Zaidan et al 2014), by keystroke (Uzun and Bicakci 2012) and by gesture, speech, handwritten text recognition and the like). Various biometric systems are being developed in such a manner (face recognition, fingerprint identification, hand geometry biometrics, retina scan, iris scan, signature, voice analysis, palm vein authentication and others).…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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Person identification in Ethnic Indian Goans using ear biometrics and neural networks

Cited by 19 publications

References 9 publications

A study of morphological variations of the human ear for its applications in personal identification

A study of morphological variations of the human ear for its applications in personal identification

Estimation of sex from the anthropometric ear measurements of a Sudanese population

Intelligent Decision Support Systems

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