Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Component Extraction and Face Recognition with Support Vector Machines
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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Human face detection is the most important process in applications such as video surveillance, human computer interface, face recognition, and image database management. Face detection algorithms have primary factors that decrease a detection ratio : variation by lighting effect, location and rotation, distance of object, complex background. Due to variations in illumination, background, visual angle and facial expressions, the problem of machine face detection is complex. Algorithms were discussed in several papers about face detection and face recognition. But we know that implementation of these algorithm is not easy. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex background. We use the YCbCr color space since it is widely used in video compression standard and multimedia streaming services. Our method detects skin regions over the entire image, and then generates face candidate based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, nose, and boundary maps for verifying each face candidate.