Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
A view-based statistical system for multi-view face detection and pose estimation
Image and Vision Computing
An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
Proceedings of the 36th annual international symposium on Computer architecture
Expert Systems with Applications: An International Journal
A MapReduce-based distributed SVM algorithm for automatic image annotation
Computers & Mathematics with Applications
Efficient and accurate face detection using heterogeneous feature descriptors and feature selection
Computer Vision and Image Understanding
A MapReduce-based distributed SVM ensemble for scalable image classification and annotation
Computers & Mathematics with Applications
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We present a face detection method using spectral histograms and support vector machines (SVMs). Each image window is represented by its spectral histogram, which is a feature vector consisting of histograms of filtered images. Using statistical sampling, we show systematically the representation groups face images together; in comparison, commonly used representations often do not exhibit this necessary and desirable property. By using an SVM trained on a set of 4500 face and 8000 nonface images, we obtain a robust classifying function for face and nonface patterns. With an effective illumination-correction algorithm, our system reliably discriminates face and nonface patterns in images under different kinds of conditions. Our method on two commonly used data sets give the best performance among recent face-detection ones. We attribute the high performance to the desirable properties of the spectral histogram representation and good generalization of SVMs. Several further improvements in computation time and in performance are discussed.