200 hand stencils (top) on the wall of Gua Tewet, Borneo and six black hand stencils (bottom) associated with the Spotted Horse mural in Pech-Merle, France. 

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... the ornately stenciled hands on the breathtaking wall panel of the Gua Tewet cave of eastern Borneo to the hands scattered around the Spotted Horses of the Pech-Merle cave in southern France, prehistoric handprints and hand stencils have fascinated archaeologists and art historians for many years ( Fig. 1). Our work is motivated by a particular interest in the sexual dimorphism of hands in parietal art [4, 14]. There is considerable anthropological interest in sexual dimorphism and sexual division of labor in Upper Paleolithic societies. For instance, if biologically modern humans pair-bonded more strongly than did the Neanderthals with whom they were competing, such pair-bonding behavior might help explain some of the competitive advantage of modern humans as well as the legacy of pair-bonding among living humans today. Much recent research into the sexual identities of the makers of prehistoric handprints and hand stencils [5] simply assumed that males made them. Recent research by Snow, however, provided evidence that females also actively participated in the cave art [14]. His initial research showed that among six hand stencils found in caves in France, four could be identified as female hands. Research in additional caves, supported by the National Geographic Society, confirmed with a larger sample that females probably made a majority (75%) of the hand stencils. Many existing methods for handprint sexual identification are limited because they are based on measurements of three fingers (index, ring, and little) and the overall hand length. First, the tedious manual measuring process inhibits studies on large-scale image data sets across age groups and ethnic populations. Unlike many other biometric data, handprint measurements across such populations are not currently available from large standard data sets and thus must be acquired according to privacy protocols. Second, the measuring process is prone to subjective errors when determining the starting and ending points of the length of the digit. Third, the absolute measurements require scale information that is consistently missing in the published photographs. To overcome these limitations, we leveraged advanced image processing and machine learning techniques in order to infer sexual identities from handprints. Our method uses normalized relative measures to achieve scale invariance, which allows the investigation of a larger portion of the publicly available data than would otherwise be available. Automatic classification of sex based on handprint images is a new technique. Sex classification based on other types of images has focused on features such as facial images [6, 12], gait analysis from walking video sequences [3], and integrated visual and audio cues [19]. Biometric security research has investigated hand-based human identification systems using various features [1, 8, 20, 21]. Sex classification based only on hand images is challeng- ing. Most people can instantly make a fairly accurate judg- ment of sex classification based on a photograph of a face. However, untrained observers often find it difficult to determine the sex based only on a handprint. It seems that for sexual identification hands contain less distinctive information than faces. Hand recognition is also different from individual human identification because any proposed approach that will successfully distinguish between two sex categories must be robust with regard to the significant variance among the hands of different subjects. The remainder of the paper is organized as follows: the classification method is described in Section 2. The experimental results are provided in Section 3. We conclude and suggest future research in Section 4. Our sex classification procedure consists of three major steps. First, the handprint image is segmented, and the hand contour is extracted. Second, points of interest (POIs), e.g., the finger tips and the valleys between the fingers, are located on the contour to compute the hand geometric features, including the lengths and widths of the fingers. These features are normalized to be insensitive to scale differences. Finally, a Support Vector Machine (SVM) [16] classifier that has been trained on manually classified hand images is used to predict the sexual identity of the hand. Our automated sex classification system needs to handle both color and gray images because handprints in artworks do not usually have skin colors. The hand segmentation algorithm must be robust enough so that fuzzy handprints from artworks can be processed. For each color hand image, we first convert it from the RGB color space to the Hue, Saturation, Value (HSV) color space due to the improved perception characteristics of the HSV space. Previous work has also shown that skin colors tend to form tighter clusters in the HSV space than in RGB space [9]. This property makes the separation between clusters easier. For gray images, the method remains the same except that we segment the hand based on image intensities instead of the color components. In the rest of the paper, to simplify the explanation, we assume the picture is colored. We perform K-means clustering on the pixel values to segment an input image into two components: the hand blob and the ...