1. Field of the Invention
The present invention relates to a parameter learning method for identifying a pattern of an input signal, such as image recognition, and a pattern identification method using the same.
2. Description of the Related Art
Many techniques have heretofore been conceived as a pattern identification method for classifying input data into predetermined classes, such as character recognition, face detection and gait authentication, and various new techniques are still being proposed with the goal of increasing the processing speed and improving the classification accuracy. For example, Viola & Jones (2001) “Rapid Object Detection using a Boosted Cascade of Simple Features”, Computer Vision and Pattern identification (hereinafter Document 1) proposes to achieve a high-speed and highly accurate pattern identification method by combining a learning method based on AdaBoost and a technique for cascade-connecting weak classifiers using a weak classifying method, which can perform computation in a short time.
Another method has also been proposed in which weak classifiers are connected in a tree structure to achieve classification into three or more classes. For example, according to Huang, Ai, Li & Lao (2005) “Vector Boosting for Rotation Invariant Multi-View Face Detection”, International Conference on Computer Vision (hereinafter, Document 2), face images to which orientation and inclination are labeled are learned, a face in a test image is detected, and its direction and inclination are determined.
As described above, techniques for performing high-speed and highly accurate pattern identification on an input image have been proposed. For example, it is required to identify the presence or absence of a face in an input image, or the presence or absence of a specific pattern (texture) with high speed and high accuracy so as to finely capture an image of a human face with an imaging apparatus or to correct a face image. However, conventional techniques as described above are not satisfactory.
USPTO Backgrounds
USPTO Backgrounds