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In this paper, non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) are used to express the local features of an object as basis vector (or basis matrix). To recognize an object through its partial appearance, it should be expressed as a combination of basis vectors (or basis matrices). We applied the part-based image representation to the vehicle recognition for autonomous driving, where occlusion is a problem. As a result, the recognition rate was about 90% which shows that the part-based representation is effective. We also propose an local similarity based on the relative ratio between vectors and showed that it is more robust against occlusion than conventional similarities such as Euclidean distance and cosine similarity. At the 60% of occlusion, the conventional similarity showed a recognition rate of 33.3%, while the local similarity showed a recognition rate of 66.7%, which means that the local similarity is superior and less sensitive to the loss of feature information due to occlusion. [ABSTRACT FROM AUTHOR]

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces. [ABSTRACT FROM AUTHOR]