Prevalence of Palm-Print Patterns and their Association with ABO Blood Group and Gender, among Medical Students of North India

Gupta, Anupma; Sharma, Rahul

doi:10.4103/jpbs.jpbs_79_22

Cited by 2 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The general distribution pattern of the primary finger print was of the same order. Gupta et al [25] and Smail et al [26] discovered similar results.…”

Section: Discussionmentioning

confidence: 57%

Relationship between Pattern of Fingerprints and Blood Groups

Aamir¹,

Masood²,

Irshad³

et al. 2022

PJMHS

View full text Add to dashboard Cite

Background and Aim: Fingerprints are a unique identification tool useful in the forensic investigation for detection of crimes. The probability of two people having an identical pattern of fingerprints is one in 64,000 million. The ridge pattern of fingerprints developed during fetal period do not change throughout their life until skin decomposes. The pattern of fingerprints differ widely with blood group variation. Therefore, the present study aimed to correlate the pattern of fingerprints with blood groups. Methodology: This prospective study was conducted on 178 medical students (MBBS and BDS) in the Department of Forensic Medicine, District Headquarters Hospital and Benazir Bhutto Hospital, Rawalpindi for the period from November 2021 to April 2022. Prior to study conduction, ethical approval was taken from the institutes research and ethical committee. Written informed consent was obtained from each individual. Participants with known blood groups having age range from 17 to 23 years were enrolled. Ink method was used for taking fingerprints. Patterns of fingerprints were categorized as loops, whirl, arches, and composite. SPSS version 25 was used for data analysis. Results: Of the total 178 medical students, there were 48 (27%) male and 130 (73%) females. The overall mean age was 21.6 ± 1.4 years with an age range from 17 to 23 years. Out of total medical students, the incidence of blood group A, B, AB, and O was 53 (29.8%), 31 (17.4%), 19 (10.7%), and 75 (42.1%) respectively. The prevalence of Rh-positive was 90.4% (n=161) among the studied cases. Loop pattern was the most prevalent fingerprints pattern observed in 95 (53.4%) cases. Majority of loop patterns were seen in blood group O participants. Other fingerprints pattern such as whorls, arches, and composite were found in 49 (27.5%), 24 (13.5%), and 10 (5.6%) respectively. Conclusion: The present study concluded that blood group “O” was the most prevalent group followed by A, B, and AB. Loop was the most common pattern of fingerprints among blood groups especially in blood group O. The prevalence of Rh-positive was found higher among the study cases. Keywords: Pattern, Fingerprints, Blood group.

show abstract

“…The general distribution pattern of the primary finger print was of the same order. Gupta et al [25] and Smail et al [26] discovered similar results.…”

Section: Discussionmentioning

confidence: 57%

Relationship between Pattern of Fingerprints and Blood Groups

Aamir¹,

Masood²,

Irshad³

et al. 2022

PJMHS

View full text Add to dashboard Cite

show abstract

“…This will result in a set of feature vectors for each image. 5. Train an XGBoost model using the feature vectors from the training set.…”

Section: Gradient Boostingmentioning

confidence: 99%

Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm

Kumar

2023

E3S Web Conf.

View full text Add to dashboard Cite

The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for each algorithm, resulting in a total sample size of 20. The comparison was carried out with a G Power of 0.8 and a confidence interval (CI) of 95% to ensure statistical significance. For this study the Birjand University Mobile Palmprint Database (BMPD) dataset was collected from the Kaggle repository, which includes a total of 1640 images containing both left and right-hand palmprints. Result: According to the results, the ResNet50 algorithm achieved a higher accuracy rate (94.7%) compared to the X Gradient Boosting algorithm (92.4%) in identifying and measuring the images. The statistical analysis indicated a significant difference between the Novel ResNet50 algorithm and X Gradient Boosting, with a pvalue of 0.003 (Independent sample T-test p<0.05). This suggests that the ResNet50 algorithm outperformed the X Gradient Boosting algorithm in this experiment. According to the study’s findings, ResNet50 is more effective in accurately identifying biometric palm prints compared to X Gradient Boosting.

show abstract

Prevalence of Palm-Print Patterns and their Association with ABO Blood Group and Gender, among Medical Students of North India

Cited by 2 publications

References 13 publications

Relationship between Pattern of Fingerprints and Blood Groups

Relationship between Pattern of Fingerprints and Blood Groups

Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm

Contact Info

Product

Resources

About