Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business profiling.Although biometric factors like gait,face,hand shape,and iris have been used to classify pe...Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business profiling.Although biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable qualities.This research employs fingerprints to classify gender,with the intention of being relevant for future studies.Several methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based classifiers.Although these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running time.In this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main components.The main goal of this study is to use CNN to extract fingerprint information.These features are then sent to an SVM classifier to determine gender.The hybrid model’s performance measures are examined and compared to those of the conventional CNN model.Using a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.展开更多
In the modern world,women now have tremendous success in every field.They can play,learn,and earn as much as men.But what about safety?Do they have the same secure environment that men and boys do?The answer is“NO”....In the modern world,women now have tremendous success in every field.They can play,learn,and earn as much as men.But what about safety?Do they have the same secure environment that men and boys do?The answer is“NO”.Women and girls have been subjected to numerous incidents,including acid throwing,rape,kidnapping,and harassment.It is common to read a lot of news like this in newspapers every day.These incidents make women feel unsafe in this society.Our freedom came a long time ago,but women still lack complete security in this society.All women cannot fight or shout all the time when some danger is happening to them.What can the physically challenged person and Children do?To make women feel safe,we designed“Wrist Band”using IoT for women safety.As the sensors sense information from the body,it will always update the information such as pulse,temperature,and vibration to the well-wishers through the Blynk app.展开更多
文摘Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business profiling.Although biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable qualities.This research employs fingerprints to classify gender,with the intention of being relevant for future studies.Several methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based classifiers.Although these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running time.In this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main components.The main goal of this study is to use CNN to extract fingerprint information.These features are then sent to an SVM classifier to determine gender.The hybrid model’s performance measures are examined and compared to those of the conventional CNN model.Using a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.
文摘In the modern world,women now have tremendous success in every field.They can play,learn,and earn as much as men.But what about safety?Do they have the same secure environment that men and boys do?The answer is“NO”.Women and girls have been subjected to numerous incidents,including acid throwing,rape,kidnapping,and harassment.It is common to read a lot of news like this in newspapers every day.These incidents make women feel unsafe in this society.Our freedom came a long time ago,but women still lack complete security in this society.All women cannot fight or shout all the time when some danger is happening to them.What can the physically challenged person and Children do?To make women feel safe,we designed“Wrist Band”using IoT for women safety.As the sensors sense information from the body,it will always update the information such as pulse,temperature,and vibration to the well-wishers through the Blynk app.
