An Incremental Learning Algorithm for Face Recognition

Abstract

In face recognition, where high-dimensional representation spaces are generally used, it is very important to take advantage of all the available information. In particular, many labelled facial images will be accumulated while the recognition system is functioning, and due to practical reasons some of them are often discarded. In this paper, we propose an algorithm for using this information. The algorithm has the fundamental characteristic of being incremental. On the other hand, the algorithm makes use of a combination of classification results for the images in the input sequence. Experiments with sequences obtained with a real person detection and tracking system allow us to analyze the performance of the algorithm, as well as its potential improvements.

Incremental Learning of Chunk Data for Online Pattern Classification Systems

“…1) if there is a new eigenaxis to be added (5) 2) otherwise (6) where , is a matrix whose column vectors correspond to the eigenvectors obtained from the previous intermediate eigenproblem, and is a -dimensional zero vector. Here, and are the new eigenvalue matrices whose diagonal elements correspond to and eigenvalues, respectively.…”

“…I N MANY real-world applications such as pattern recognition, data mining, and time-series prediction, we often confront difficult situations where a complete set of training samples is not given when constructing a system. In face recognition, for example, since human faces have large variations due to expressions, lighting conditions, makeup, hairstyles, and so forth, it is hard to consider all variations of face in advance [6], [31], [35]. In many cases, training samples are provided only when a system misclassifies objects; hence the system is learned online to improve the classification performance.…”