Gender Recognition from Unconstrained and Articulated Human Body

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition

Gender Recognition and Appearance Description in Unconstrained Images of Human Body

Gender recognition has many useful applications, ranging from business intelligence, through to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. In this work, we propose a novel problem of gender recognition in articulated human body images acquired from an unconstrained environment in the real world. Our empirical study answers the question of whether gender recognition can be performed in articulated body images, and discovers important issues such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition. We also pursue data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases that have not been explored before for gender recognition. At the end of this work, we also present some preliminary results on the automatic description of the appearance of the upper body

Human body radiation wave analysis and classification for gender and body segments recognition / Siti Zura A. Jalil @ Zainuddin

This thesis presents a novel analysis and classification of human radiation wave for gender and body segments recognition. The human body has been shown to emit radiation into space surrounding their body. The research study frequency radiations at 23 points of the human body segregated into body segments of Chakra, Left, Right, Upper body, Torso, Arm and Lower body. Initially, the characteristics of frequency radiation are examined using statistical tools to find the correlations between variables. Multivariate analysis of variance (MANOVA) is employed to compare the differences of frequency radiation characteristics between genders. Then, the classification algorithm of A:-nearest neighbor (KNN) is employed to discriminate between genders, and between body segments. The classifiers are evaluated through analysis of the performance indicators applied in medical research of accuracy, precision, sensitivity and specificity in receiver operating characteristics (ROC) analysis. The findings obtained from this research show that the wave radiation characteristics of a male and a female human body are different. The proposed technique is able to distinguish gender and classify body segments, and it is justified using MANOVA statistical tests. The individual features of gender differences using analysis of variance forms a significant outcome on 13 points that are located close to the forehead, left and right side of abdomen, palms, arms, shoulders and head. In KNN classification, the outcomes for the classifiers are consistent with the MANOVA. For gender recognition, the classifiers have successfully differentiated male from female human body, and achieving a performance of 100% for accuracy, sensitivity and specificity. For body segment recognition, the classifiers are also able to distinguish between the body segments producing 100% accuracy in classifying of Chakra, Left and Right, whilst 93.75% accuracy is obtained in classifying of Upper body, Torso, Arm and Lower body. The sensitivity and specificity computed for body segment recognition are found to be more than 80% indicating a good classification performance. The outcomes of this study demonstrate that a male and a female human body, and also the different body segments, have different frequency radiation characteristics. The finding offers new opportunities in research and application based on human body radiation such as biometrics and surveillance systems

Classification of Aedes Adults Mosquitoes in Two Distinct Groups Based on Fisher Linear Discriminant Analysis and FZOARO Techniques

This paper describes the breeding, feeding and measurement of Aedes mosquitoes based on body size (wing length). Due to similarity in body size measurements, we were constrained on gender recognition. To reveal the gender identity of these mosquitoes, Fisher linear discriminant analysis and FZOARO classification models were considered suitable for prediction and classification.   We randomly selected 15 mosquitoes from each groups and categorize the body size as small and large and applied the classification procedures. Both classification techniques perform similar. The numerical simulation reveals that 86.67% were classified as male for group one and 80% were correctly classified as female in group two.   Keywords: Fisher linear discriminant analysis; FZOARO; Classification

Classification of Aedes Adults Mosquitoes in Two Distinct Groups Based on Fisher Linear Discriminant Analysis and FZOARO Techniques

This paper describes the breeding, feeding and measurement of Aedes mosquitoes based on body size (wing length). Due to similarity in body size measurements, we were constrained on gender recognition. To reveal the gender identity of these mosquitoes, Fisher linear discriminant analysis and FZOARO classification models were considered suitable for prediction and classification.   We randomly selected 15 mosquitoes from each groups and categorize the body size as small and large and applied the classification procedures. Both classification techniques perform similar. The numerical simulation reveals that 86.67% were classified as male for group one and 80% were correctly classified as female in group two. Keywords: Fisher linear discriminant analysis; FZOARO; Classification