The paper presents an example-based learning approach for detecting vertical frontal views of human faces in complex scenes. The method models the distribution of human face patterns using a few view-based "face" and "nonface" model clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines whether a human face exists based on these difference feature vectors. The distance metric used for computing the difference feature vectors and the "nonface" clusters included in the distribution-based model are critical for the system's success. The system is evaluated on two test databases, achieving high detection rates with low false-alarm rates, even under varying lighting conditions and complex backgrounds. The paper also discusses the impact of different classifier architectures, distance metrics, and the presence of "nonface" clusters on the system's performance.The paper presents an example-based learning approach for detecting vertical frontal views of human faces in complex scenes. The method models the distribution of human face patterns using a few view-based "face" and "nonface" model clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines whether a human face exists based on these difference feature vectors. The distance metric used for computing the difference feature vectors and the "nonface" clusters included in the distribution-based model are critical for the system's success. The system is evaluated on two test databases, achieving high detection rates with low false-alarm rates, even under varying lighting conditions and complex backgrounds. The paper also discusses the impact of different classifier architectures, distance metrics, and the presence of "nonface" clusters on the system's performance.