Finding naked people

Abstract

This paper demonstrates a content-based retrieval strategy that can tell whether there are naked people present in an image. No manual intervention is required. The approach combines color and texture properties to obtain an effective mask for skin regions. The skin mask is shown to be effective for a wide range of shades and colors of skin. These skin regions are then fed to a specialized grouper, which attempts to group a human figure using geometric constraints on human structure. This approach introduces a new view of object recognition, where an object model is an organized collection of grouping hints obtained from a combination of constraints on geometric properties such as the structure of individual parts, and the relationships between parts, and constraints on color and texture. The system is demonstrated to have 60% precision and 52% recall on a test set of 138 uncontrolled images of naked people, mostly obtained from the internet, and 1401 assorted control images, drawn from a wide collection of sources.

Constructing Face Image Logs that are Both Complete and Concise

“…In either case, such images should score poorly in overall quality. Research conducted by Margaret Fleck, David Forsyth and Chris Bregler reveals that pixels representing flesh tones are tightly clustered in a small region of hue-saturation color space [2]. Results for this paper were obtained using the region containing all hues between −30 o and 30 o , saturated between 5% and 95%.…”

From Maximum Entropy to Belief Propagation: An application to Skin Detection

“…It plays an important role in various applications such as face detection [14], searching and filtering image content on the web [6].…”

“…One method is to define explicitly the skin color boundaries in some color space [6]. The simplicity of this method leads to a fast classifier.…”

Protecting Privacy from Continuous High-Resolution Satellite Surveillance

“…They include (1) filtering of images of naked people using a skin filter and a human figure grouper [3,4], and (2) using a content-based feature vector indexing where an image is matched against a small number of feature vectors obtained from a training database [16,17]. However, these approaches filter all images that match a set of criteria, but do not provide controlled access that facilitates access to images for legitimate users.…”