Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Robust Real-Time Face Detection
International Journal of Computer Vision
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
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One of the most prolific topics of research in the field of computer vision is pattern detection in images. A large number of practical applications for face detection exist. Contemporary work even suggests that any of the results from specialized detectors can be approximated by using fast detection classifiers. In this project, we developed an algorithm which detected faces from the input image with a lower false detection rate and lower computation cost using the ensemble effects of computer vision concepts. This algorithm utilized the concepts of recognizing skin color, filtering the binary image, detecting blobs and extracting different features from the face. The result is supported by the statistics obtained from calculating the parameters defining the parts of the face. The project also implements the highly powerful concept of Support Vector Machine that is used for the classification of images into face and non-face class. This classification is based on the training data set and indicators of luminance value, chrominance value, saturation value, elliptical value and eye and mouth map values.