Abstract
We present a new face detection algorithm based on a first-order reduced Coulomb energy (RCE) classifier. The algorithm locates frontal views of human faces at any degree of rotation and scale in complex scenes. The face candidates and their orientations are first determined by computing the Hausdorff distance between simple face abstraction models and binary test windows in an image pyramid. Then, after normalizing the energy, each face candidate is verified by two subsequent classifiers: a binary image classifier and the first-order RCE classifier. While the binary image classifier is employed as a preclassifier to discard nonfaces with minimum computational complexity, the first-order RCE classifier is used as the main face classifier for final verification. An optimal training method to construct the representative face model database is also presented. Experimental results show that the proposed algorithm yields a high detection ratio while yielding no false alarm.
Highlights
In recent years, due to the potential applications in many fields, including surveillance, authentication, video indexing, and so forth, face detection and recognition problems have gained much attention in computer vision society
In order to optimize the number of representative face models, we applied the sequential forward selection (SFS) algorithm [2] for selecting representative face models among face samples
The proposed face detection algorithm has been tested on the Carnegie Mellon University (CMU) face image database http://vasc.ri.cmu.edu/ idb/html/face/profile images/index.html, which consists of 50 images, containing 223 faces of arbitrary scales and rotations
Summary
Due to the potential applications in many fields, including surveillance, authentication, video indexing, and so forth, face detection and recognition problems have gained much attention in computer vision society. Well-organized parametric classifiers, such as Bayesian classifier [3], artificial neural network [4, 5], support vector machine [6, 7], have been used to classify the feature vectors by supervised classification techniques in the feature space. These parametric classifiers for face detection use high degree of data abstraction such as a set of trained weights, coefficients, or probabilities. Those parameters are extracted from the training sample faces
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