2009 |
Štruc, Vitomir; Gajšek, Rok; Mihelič, France; Pavešić, Nikola Using regression techniques for coping with the one-sample-size problem of face recognition Journal Article In: Electrotechnical Review, vol. 76, no. 1-2, pp. 7-12, 2009. Abstract | Links | BibTeX | Tags: biometrics, face recognition, one sample size problem, regression techniques, small sample size @article{EV-Struc_2009,There is a number of face recognition paradigms which ensure good recognition rates with frontal face images. However, the majority of them require an extensive training set and degrade in their performance when an insufficient number of training images is available. This is especially true for applications where only one image per subject is at hand for training. To cope with this one-sample-size (OSS) problem, we propose to employ subspace projection based regression techniques rather than modifications of the established face recognition paradigms, such as the principal component or linear discriminant analysis, as it was done in the past. Experiments performed on the XM2VTS and ORL databases show the effectiveness of the proposed approach. Also presented is a comparative assessment of several regression techniques and some popular face recognition methods. |
2008 |
Štruc, Vitomir; Mihelič, France; Gajšek, Rok; Pavešić, Nikola Regression techniques versus discriminative methods for face recognition Proceedings Article In: Proceedings of the 9th international PhD Workshop on Systems and Control, pp. 1-5, Izola, Slovenia, 2008. Abstract | Links | BibTeX | Tags: biometrics, face recognition, face verification, modeling, performance evaluation, regression techniques @inproceedings{PHD2008,In the field of face recognition it is generally believed that ”state of the art” recognition rates can only be achieved when discriminative (e.g., linear or generalized discriminant analysis) rather than expressive (e.g., principal or kernel principal component analysis) methods are used for facial feature extraction. However, while being superior in terms of the recognition rates, the discriminative techniques still exhibit some shortcomings when compared to the expressive approaches. More specifically, they suffer from the so-called small sample size (SSS) problem which is regularly encountered in the field of face recognition and occurs when the sample dimensionality is larger than the number of available training samples per subject. In this type of problems, the discriminative techniques need modifications in order to be feasible, but even in their most elaborate forms require at least two training samples per subject. The expressive approaches, on the other hand, are not susceptible to the SSS problem and are thus applicable even in the most extreme case of the small sample size problem, i.e., when only one training sample per subject is available. Nevertheless, in this paper we will show that the recognition performance of the expressive methods can match (or in some cases surpass) that of the discriminative techniques if the expressive feature extraction approaches are used as multivariate regression techniques with a pre-designed response matrix that encodes the class membership of the training samples. The effectiveness of the regression techniques for face recognition is demonstrated in a series of experiments performed on the ORL database. Additionally a comparative assessment of the regression techniques and popular discriminative approaches is presented. |