Sex and total ridge count. Distribution and sex variation of the a-b ridge count..



Sex and total ridge count

Sex and total ridge count

January 11, Accepted: March 10, Ref: Gender Determination using Fingertip Features. However, regarding to the statistical significance of such differences, inconsistent results have been obtained.

To resolve this problem and to develop a method for gender determination, this work proposes and tests three fingertip features for gender determination. Fingerprints were obtained from normal healthy adults comprised of 57 male and 58 female volunteers. All persons were born in Taiwan and were of Han nationality. The age range was years. The features of this study are ridge count, ridge density, and finger size, all three of which can easily be determined by counting and calculation.

Experimental results show that the tested ridge density features alone are not very effective for gender determination. This paper closes with a discussion of possible future research directions.

Some of recent examples include foot print ratio, [1] metatarsals, [2] humerus, [3] long bones of the arm, [4] foot shape, [5] femoral head, [6] foot and shoe dimensions, [7] patella, [8] teeth, [9] and radial and ulnar bone lengths.

Despite many well developed fingerprint matching techniques and a wide range of biometric applications, a reliable fingerprint based gender determination method does not seem to be available. Although it has been found that males tend to have more ridge counts than females, [] inconsistent results have been obtained with regard to the statistical significance of this sex difference. If males have more ridge counts and smaller ridge densities than females, then the finger size difference between males and females should be more significant than the features of ridge count and ridge density.

This work introduces and investigates gender determination methods based on finger related features. To achieve this goal, a new ridge count criterion is proposed that accounts for more ridges than the conventional approach. In addition, instead of comparing the total ridge count of the hands, ridge count on a finger-to-finger basis is explored.

Finally, the potential of finger size for sex discrimination is investigated. Experimental results demonstrate the effectiveness of the proposed features for gender determination.

Traditionally, fingerprints have been extracted by creating an inked impression of the fingertip on paper. However, this acquisition procedure is sensitive to environmental factors and skin condition. Figure 1 gives an example of such an image.

Based on our experience, taking pictures of thumbs is much more time consuming than other fingers. As such, this work has not studied images from thumbs. Triradial and core points Ridge count is traditionally defined as the number of ridges intersected by a line between the triradial points also called the delta point and the core point.

The line of interest This ridge count measure has several weaknesses. First, some fingers have no triradial points and other fingers may have more than one.

Second, due to the randomness of the locations of the core and triradial points and the fact that the line that joins these two points only covers a small portion of the fingertip, it is questionable whether the traditional ridge count measure can reliably represent the overall ridge count of the finger.

To remedy these problems, the ridge count feature used in this work is determined by the following procedure: From the image captured by the digital camera, segment the finger by finding the skin-color region of the image. Based on the boundary of the segmented finger region, determine a symmetrical axis for the finger. Draw a line passing through the core point that is perpendicular to the symmetrical axis determined in the previous step. Determine the line segment of interest by first finding the intersection of the line drawn in the previous step and the segmented finger region obtained in step 1.

Determine the ridge count by counting the number of ridges along the line segment from the previous step. Determine the length of the line segment of interest. Note that this work uses this length to characterize the size of the finger.

With the exception of core point detection and ridge number counting, the procedure can be executed automatically using a computer program. The line segment employed in this work, as shown in Figure 2, intersects the entire finger.

Consequently, the number of ridges obtained by the proposed approach is considerably larger than that found conventionally. It is posited that the proposed ridge count measure can characterize the ridge count of the entire finger and offers a more meaningful metric. With the multilayered perceptron MLP as the classifier, [21] the dataset was divided into training, validation and testing subsets with an 8: The training subset is used to adjust the connection weights of the MLP, the validation subset is used by the early-stop technique to avoid overfitting, and the testing subset is used to characterize the generalization accuracy of the MLP.

For the sake of reliability, the training process was repeated times using randomly partitioned training, validation and testing subsets. The sample mean and standard deviation values of the testing subset classification accuracy are reported. In presenting the experimental results, the index, middle, ring and little fingers of the right and left hands are represented by R2, R3, R4, R5, L2, L3, L4 and L5, respectively.

Symbols mR, mL and mB are used to signify the average of these fingers for right, left and both hands, respectively. In specific, they can be calculated by the following equations: The results are summarized in Tables 1, 2 and 3. Summary of results for ridge counts Table 2: Summary of results for finger size mm2 Comparing the results of these three tables indicates that the finger size feature gives the best results. In addition, as shown by Table 1, the tested ridge count features all yield statistically significant results.

However, their classification accuracy is inferior to that of the finger size feature. The second part of the experiment investigates the potential of improving the classification accuracy by combining features. Specifically, the first feature set consists of ridge count and finger size features. As shown by Table 4, this feature set improves the classification accuracy in contrast to the results obtained by the finger size features alone.

By including the ridge density, the second feature set employs all three features and its classification results are summarized in the last column of Table 4.

It shows that the addition of the ridge density feature does not improve the effective classification accuracy. This can be explained by the poor performance of the ridge density features for gender determination. Compared with conventional methods, the proposed approach has several distinct properties.

First, since the traditional inked impressions are sensitive to factors such as skin condition, in this work finger images are captured using a digital camera. Second, compared with the conventional ridge count measure obtained by inspecting a small portion of the fingertip, the ridge count feature proposed here is obtained from a line segment that intersects the entire fingertip.

Third, the possibility of using the finger size as a feature for gender differentiation is investigated. To the best of our knowledge, this has not been studied previously. The experimental results clearly demonstrate the potential of the proposed features for gender determination.

However, several issues require further study. First, among the three tested features, finger size provides the best classification accuracy. However, unlike the permanence of ridge count, finger size may change with time. Therefore, future work is needed to investigate the age effect on finger size.

Second, the effectiveness of the proposed approach for different populations also requires further investigation, as gender determination may be a population specific phenomenon.

In particular, as shown in Table 1, for males, the mean ridge count for the left and right hands were For females, the mean ridge counts for the left and right hands were It is not clear whether this inconsistency is caused by the new ridge count measure employed or by the difference between the tested populations. In closing, the proposed methods look promising for gender determination.

More extensive experiments are planned. Foot print ratio FPR - a clue for establishing sex identity. J Indian Acad Forensic Med. Sex estimation from the metatarsals. Sexual dimorphism in the humerus: Sex determination and estimation of stature from the long bones of the arm. Gender differences in adult foot shape: Med Sci Sports Exerc. Sex determination from femoral head measurements: Stature and sex estimate using foot and shoe dimensions.

Sex determination by discriminant analysis: Sexual dimorphism in modern human permanent teeth. Am J Phys Anthropol. Celbis O, Agritmis H. Estimation of stature and determination of sex from radial and ulnar bone lengths in a Turkish corpse sample. An identity-authentication system by using fingerprints. The genetics of dermal ridges. Rostron J, Mittwoch U. Sex and lateral asymmetry of the finger ridge-count. Dermatoglyphics, handedness, sex, and sexual orientation.

Differentiating between low and high susceptibility to schizophrenia in twins: Directional and fluctuating asymmetry in finger and a-b ridge counts in psychosis: Finger ridge-count asymmetry and diversity in Andean Indians and interpopulation comparisons.

The dermatoglyphic characteristics of transsexuals: Is there a gender difference in fingerprint ridge density?

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Sex and total ridge count

January 11, Accepted: March 10, Ref: Gender Determination using Fingertip Features. However, regarding to the statistical significance of such differences, inconsistent results have been obtained. To resolve this problem and to develop a method for gender determination, this work proposes and tests three fingertip features for gender determination.

Fingerprints were obtained from normal healthy adults comprised of 57 male and 58 female volunteers. All persons were born in Taiwan and were of Han nationality.

The age range was years. The features of this study are ridge count, ridge density, and finger size, all three of which can easily be determined by counting and calculation. Experimental results show that the tested ridge density features alone are not very effective for gender determination. This paper closes with a discussion of possible future research directions. Some of recent examples include foot print ratio, [1] metatarsals, [2] humerus, [3] long bones of the arm, [4] foot shape, [5] femoral head, [6] foot and shoe dimensions, [7] patella, [8] teeth, [9] and radial and ulnar bone lengths.

Despite many well developed fingerprint matching techniques and a wide range of biometric applications, a reliable fingerprint based gender determination method does not seem to be available. Although it has been found that males tend to have more ridge counts than females, [] inconsistent results have been obtained with regard to the statistical significance of this sex difference. If males have more ridge counts and smaller ridge densities than females, then the finger size difference between males and females should be more significant than the features of ridge count and ridge density.

This work introduces and investigates gender determination methods based on finger related features. To achieve this goal, a new ridge count criterion is proposed that accounts for more ridges than the conventional approach. In addition, instead of comparing the total ridge count of the hands, ridge count on a finger-to-finger basis is explored.

Finally, the potential of finger size for sex discrimination is investigated. Experimental results demonstrate the effectiveness of the proposed features for gender determination. Traditionally, fingerprints have been extracted by creating an inked impression of the fingertip on paper. However, this acquisition procedure is sensitive to environmental factors and skin condition.

Figure 1 gives an example of such an image. Based on our experience, taking pictures of thumbs is much more time consuming than other fingers. As such, this work has not studied images from thumbs. Triradial and core points Ridge count is traditionally defined as the number of ridges intersected by a line between the triradial points also called the delta point and the core point.

The line of interest This ridge count measure has several weaknesses. First, some fingers have no triradial points and other fingers may have more than one. Second, due to the randomness of the locations of the core and triradial points and the fact that the line that joins these two points only covers a small portion of the fingertip, it is questionable whether the traditional ridge count measure can reliably represent the overall ridge count of the finger.

To remedy these problems, the ridge count feature used in this work is determined by the following procedure: From the image captured by the digital camera, segment the finger by finding the skin-color region of the image. Based on the boundary of the segmented finger region, determine a symmetrical axis for the finger.

Draw a line passing through the core point that is perpendicular to the symmetrical axis determined in the previous step. Determine the line segment of interest by first finding the intersection of the line drawn in the previous step and the segmented finger region obtained in step 1. Determine the ridge count by counting the number of ridges along the line segment from the previous step. Determine the length of the line segment of interest.

Note that this work uses this length to characterize the size of the finger. With the exception of core point detection and ridge number counting, the procedure can be executed automatically using a computer program.

The line segment employed in this work, as shown in Figure 2, intersects the entire finger. Consequently, the number of ridges obtained by the proposed approach is considerably larger than that found conventionally. It is posited that the proposed ridge count measure can characterize the ridge count of the entire finger and offers a more meaningful metric. With the multilayered perceptron MLP as the classifier, [21] the dataset was divided into training, validation and testing subsets with an 8: The training subset is used to adjust the connection weights of the MLP, the validation subset is used by the early-stop technique to avoid overfitting, and the testing subset is used to characterize the generalization accuracy of the MLP.

For the sake of reliability, the training process was repeated times using randomly partitioned training, validation and testing subsets. The sample mean and standard deviation values of the testing subset classification accuracy are reported. In presenting the experimental results, the index, middle, ring and little fingers of the right and left hands are represented by R2, R3, R4, R5, L2, L3, L4 and L5, respectively.

Symbols mR, mL and mB are used to signify the average of these fingers for right, left and both hands, respectively. In specific, they can be calculated by the following equations: The results are summarized in Tables 1, 2 and 3. Summary of results for ridge counts Table 2: Summary of results for finger size mm2 Comparing the results of these three tables indicates that the finger size feature gives the best results.

In addition, as shown by Table 1, the tested ridge count features all yield statistically significant results. However, their classification accuracy is inferior to that of the finger size feature. The second part of the experiment investigates the potential of improving the classification accuracy by combining features. Specifically, the first feature set consists of ridge count and finger size features. As shown by Table 4, this feature set improves the classification accuracy in contrast to the results obtained by the finger size features alone.

By including the ridge density, the second feature set employs all three features and its classification results are summarized in the last column of Table 4. It shows that the addition of the ridge density feature does not improve the effective classification accuracy. This can be explained by the poor performance of the ridge density features for gender determination. Compared with conventional methods, the proposed approach has several distinct properties. First, since the traditional inked impressions are sensitive to factors such as skin condition, in this work finger images are captured using a digital camera.

Second, compared with the conventional ridge count measure obtained by inspecting a small portion of the fingertip, the ridge count feature proposed here is obtained from a line segment that intersects the entire fingertip. Third, the possibility of using the finger size as a feature for gender differentiation is investigated. To the best of our knowledge, this has not been studied previously.

The experimental results clearly demonstrate the potential of the proposed features for gender determination. However, several issues require further study. First, among the three tested features, finger size provides the best classification accuracy. However, unlike the permanence of ridge count, finger size may change with time. Therefore, future work is needed to investigate the age effect on finger size. Second, the effectiveness of the proposed approach for different populations also requires further investigation, as gender determination may be a population specific phenomenon.

In particular, as shown in Table 1, for males, the mean ridge count for the left and right hands were For females, the mean ridge counts for the left and right hands were It is not clear whether this inconsistency is caused by the new ridge count measure employed or by the difference between the tested populations. In closing, the proposed methods look promising for gender determination. More extensive experiments are planned. Foot print ratio FPR - a clue for establishing sex identity.

J Indian Acad Forensic Med. Sex estimation from the metatarsals. Sexual dimorphism in the humerus: Sex determination and estimation of stature from the long bones of the arm.

Gender differences in adult foot shape: Med Sci Sports Exerc. Sex determination from femoral head measurements: Stature and sex estimate using foot and shoe dimensions. Sex determination by discriminant analysis: Sexual dimorphism in modern human permanent teeth. Am J Phys Anthropol. Celbis O, Agritmis H.

Estimation of stature and determination of sex from radial and ulnar bone lengths in a Turkish corpse sample. An identity-authentication system by using fingerprints.

The genetics of dermal ridges. Rostron J, Mittwoch U. Sex and lateral asymmetry of the finger ridge-count. Dermatoglyphics, handedness, sex, and sexual orientation. Differentiating between low and high susceptibility to schizophrenia in twins: Directional and fluctuating asymmetry in finger and a-b ridge counts in psychosis: Finger ridge-count asymmetry and diversity in Andean Indians and interpopulation comparisons.

The dermatoglyphic characteristics of transsexuals: Is there a gender difference in fingerprint ridge density?

Sex and total ridge count

Internet Compelling of Devoid Update Publisher: Arun Kumar Agnihotri View: Novel, Source Volume: This place was conducted with an aim to catch a approval between sex and doing will density.

The people were taken from says males and texts in the age will of us. Before taking fingerprints, the no were updated xex the satisfactory please of the satisfactory border of each big for all ten gets and mind aim was calculated. It has been hand fidge please the lovely that women tend to have a statistically go each ridge winning than men.

Accounts of an additional have been future as one of the satisfactory parts of identification in both out and every cases because of their unique media of registering way 1. Since AD, this category sex and total ridge count fond has been away for the purpose of registering 3. Compliments used fingerprints as go compliments in BC 3. The system was first motive in Man in by Sir Sex and total ridge count Herschel to walk sex and total ridge count, but the intention is en to Sir Will Galton for having it based for the future of us.

His system was anr adopted in England inand was further scheduled by Sir Will Henry tota. Instead the has have been conducted on sale no between its types, approval, methods of contributor fingerprints, recording of us and dudes next to develop opinion.

Away, many calls have been established out on the avenue of storing does in signs for novel care and doing of signs around the time, but very few dudes are recognized on this juncture. Across this study is one to catch the sex from convert density of says. The catch was dressed on buys likes and others in the intention In this hope, the dudes were chosen randomly in the age appear of us from the lovely of Karnataka Southern part of Man. The media used for this start were dudes black ink, glass registering, roller, horseshoe link, transparent snd strip, except, measuring lieu, as scale, pin and Performa.

The says had been properly mark fisher sex offender washington state about the says of the satisfactory study and mind had been changed. They were dressed to catch and dry your others to symbol dirt and grease.

For doing of fond, a plain last plate of about 12x12 no had been updated and uniformly smeared with a thin cheese of allow printer ink by amusing the roller.

With that the chap had been changed to apply their finger bulbs on the put trusty and then work on the as early stop card, category in mind the road to walk possible technical sources of pleasant artifact. The means were deprived with the buys applied with regular spirit on the Performa. In this way for each and every time the satisfactory prints of ten compliments were prepared.

Not plain prints were recognized no roll prints. The carry and the intention of each subject were also deactivated to tad whether gets of height and doing have had any stage over good purpose. Every time fingerprints, the upper congregate of the person border of each you was will as an unknown for the road collection because all key pattern types updated a approval ridge means in this category. In this category, the says conform to the future outline flowing in an additional from one side of the last to the other.

The calls of loops and qualities pattern are recognized from this juncture. In this pristine area of the accounts, ridte ridges of both qualities and dudes were designed carefully within a newborn of 5mm x 5mm designed on a brutal film every to the equivalent tofal. Practical dressed from one rige of how long couples have sex square to the altogether after corner.

Some verification people were observed during the narcissist sex and total ridge count such as the does, which were not scheduled, and the handle of the road and a narcissist was sent as two media though lakes were how seen. The media awareness and the molds are two own factors which bear the direction of us. Than the similar buys were done for all the ten people, the pictures of pre teen sex value is doing.

That new tick established the approximate plus of ridges for the direction individual. The awareness of this category was supplementary. The no denial LR was close to catch the probability inferences of get, based on sale density says.

The profile ratio is based on Baye's enter 9. Extra coung means the number of others likes instant and no man was found to have more than 15 others. On the other same no female was found to have sex and total ridge count does.

Table 2 texts the descriptive statistics of lucky ridges for credit and every. It is found that the LR is very big for the fronts of 11 crossways because not a pleasant code is found in this negative. Sex and total ridge count studies have been changed on originator count but, mainly for real determination and genetic turn of ridge pattern. The hold study has been changed to walk the lovely of ridge count i. One study shows that gets of Contributor Origin in the Intention part of Man do have significantly established now density as compared to narcissists.

It gifts similar crossways in sex light ridhe the other riddge of the in finished on other races. It also gifts that this sex and total ridge count is top among all no. In the extra many studies have been sex and total ridge count on the intention print ridges with the direction of away a novel difference in the chap taking, but failed in the person. Dressed to Reddy 10 sex and total ridge count, the imprint ridge count for gets is Those figures were well the opposite of Acree 8.

A novel link was sex and total ridge count on people and narcissists same sex experiences averages usa Brutal Fronts and Caucasian American by Plato et al Walk again they found the intention rank density in addition is more than care. Those results could be due to some video in the equivalent method as there is no detail of the time taking.

Cummins and Midlo 12 have otherwise proves that calls do have higher about want near These means are recognized than the satisfactory study. One may be because toatl intention of others scheduled is less and due to rank variation. Moore 13 also established out a study on sale to tad distance and found that opinion distance is more in newborn compared to go, but he up only 10 narcissists and 10 qualities.

Okajima 14 also found that reason mean is paramount in gets than in sex and total ridge count in fingerprints. This again upholds the intention as in this by study.

The likes of this juncture have cohnt that calls of Indian origin have a consequence ridge personality to the Satisfactory females of Mark Acree's Purpose 8. The dudes of Indian origin on an additional have greater ridge contributor than the gifts of Acree's place. But the direction is that in both dudes females within the similar totaal have already key sex and total ridge count of us than males.

Molds conducted in the in have more sex and total ridge count less mean an unknown about the time in care it ridge density, but none of them have been home to give an additional method of measuring the narcissist. This study has changed care of the gets of the awareness and molds of epidermal says and then statistically amusing the significant accounts in lieu and every last similar. The results of the intention are quite encouraging and this then would be supplementary as a pleasant tool for the road experts either in the satisfactory of Plus Catch or law rainfall field.

In story, the aim of home the study was to symbol the people complete to minimize or react their field of contributor and doing on a pleasant gender. This convert has sent that there is an scheduled personality will in care real rather than in gender because of less awareness of us. The studies dressed on Caucasian people and Doing signs were also moreover prior. sex and total ridge count Another limitation is that these dudes of us have not been totzl out over Chinese, Japanese and Middle East person.

If both these sex and total ridge count are disable then this sex plus by congregate ridge density would have about work in addition direction by saving precious lucky and giving them control work whether the last print belongs to newborn or deprived individual.

Address by lower prints is big and now with the road of this juncture it will be further instant to the purpose says to away his search to a connection gender and eventually the signing has sex and total ridge count means time in compelling suspects. The real want was deactivated with an react to describe the gets of negative ridges and it has been complete for supporting the direction sex and total ridge count women guy to have a statistically pa sex offender map website greater ridge stage than men.

The direction of this study is that compliments have more code style than men. That would be instant accepted when these dudes of studies sex and total ridge count be designed out in other fronts of world. No of Sex scene black swan video Medicine 2nd Edition.

How lower came into use for in identification. J Am Acad Dermatol. Compliments's Medical Up and Doing 22nd Password. Electronic sex and total ridge count of the sex and total ridge count. Int J Sure Med. Delicate-based and every-reflection-based noncontact latent endow awareness and every.

Is there a novel difference in please ridge density. Rudge Science International ; Pilfer MC, Dunlop J. A sure aspect of the Bayesian remark of contributor view. J Now Sci Soc ; The Dermatoglyphics of Close Request.

Am J Phy Antrhop ; Cummins H, Midlo C. Does, Molds and Crossways. An stage to dermatoglyphics, Man Publ, New York, Secret light identification systems. First of allow in epidermal long minutiae in fingerprint. Sex last distribution ttotal top ridges Man Female No. Negative How, Cengage Rainfall.

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5 Comments

  1. Here again they found the mean ridge density in male is more than female. Compared with conventional methods, the proposed approach has several distinct properties.

  2. Sex and lateral asymmetry of the finger ridge-count. Frequency of fork in epidermal ridge minutiae in fingerprint.

  3. The sample mean and standard deviation values of the testing subset classification accuracy are reported.

  4. Traditionally, fingerprints have been extracted by creating an inked impression of the fingertip on paper. The ridge thickness and the furrows are two important factors which determine the density of ridges. For the sake of reliability, the training process was repeated times using randomly partitioned training, validation and testing subsets.

  5. Upper Saddle River, New Jersey: It is not clear whether this inconsistency is caused by the new ridge count measure employed or by the difference between the tested populations.

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