Human automated target recognition (ATR) capability holds important tactical value in military as well as civilian applications. Unmanned systems equipped with real-time human ATR sensors and software will serve to detect potential threats before human forces encounter them. Unattended ground stations may use human ATR in search and rescue applications, to alert rescue teams when help is necessary. The algorithm proposed in this study utilizes infrared imagery to detect people based on the radiance and shape of the human head. The algorithm works in a three-step process of segmentation, feature extraction, and classification. First, the IR image is segmented to reveal only human skin areas (e.g., arms, legs, heads). Next, three morphological features are extracted from each segmented object of interest. Finally, a classifier will use the features to determine whether the object is a head or a nonhead, based on previous algorithmic training. Two types of classifiers were tested in this study: a k-nearest-neighbor classifier and various neural networks. Results show that using a neural network classifier, 97% accuracy in head identification is possible after examining two sequential uncorrelated frames containing the same human head in different views. Tests in a desert environment at nighttime show that the majority of test subjects are detected, with few false positives.