Estudio experimental sobre la combinación temporal de resultados en el reconocimiento de caras con secuencias de video

[ES]Se ha comprobado que la combinación de los resultados de clasi cación de varias imágenes de una secuencia mejora la probabilidad de acierto en el problema del reconocimiento de caras. No obstante, queda por dilucidar qué método de agregación temporal de los resultados es el más apropiado para cada caso concreto. En sistemas prácticos el método de combinación debe además ser simple para no consumir mucho tiempo de cómputo, teniendo en cuenta que el sistema tendrá otras etapas de proceso con una latencia relativamente alta. En este trabajo se describe un estudio experimental de varios métodos de combinación...

An incremental learning algorithm for face recognition

[EN]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.

Fast classification in incrementally growing spaces

[EN]The classification speed of state-of-the-art classifiers such as SVM is an important aspect to be considered for emerging applications and domains such as data mining and human-computer interaction. Usually, a test-time speed increase in SVMs is achieved by somehow reducing the number of support vectors, which allows a faster evaluation of the decision function. In this paper a novel approach is described for fast classification in a PCA+SVM scenario. In the proposed approach, classification of an unseen sample is performed incrementally in increasingly larger feature spaces. As soon as the classification confidence is above a threshold the process stops and the class label is retrieved...