SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Introduction to Algorithms
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
IEEE Transactions on Image Processing
Mixture of experts for classification of gender, ethnic origin, and pose of human faces
IEEE Transactions on Neural Networks
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The sexual identities of human handprints inform hypotheses regarding the roles of males and females in prehistoric contexts. Sexual identity has previously been manually determined by measuring the ratios of the lengths of the individual's fingers as well as by using other physical features. Most conventional studies measure the lengths manually and thus are often constrained by the lack of scaling information on published images. We have created a method that determines sex by applying modern machine-learning techniques to relative measures obtained from images of human hands. This is the known attempt at substituting automated methods for time-consuming manual measurement in the study of sexual identities of prehistoric cave artists. Our study provides quantitative evidence relevant to sexual dimorphism and the sexual division of labor in Upper Paleolithic societies. In addition to analyzing historical handprint records, this method has potential applications in criminal forensics and human-computer interaction.