Pablo H. Hennings Yeomans

We propose a palmprint classification algorithm with the use of multiple correlation filters per class. Correlation filters are two-class classifiers that produce a sharp peak when filtering a sample of their class and a noisy output otherwise. For every class, we train the filters for a palm at different locations, where the palmprint region has a high degree of line content. With the use of a line detection procedure and a simple line energy measure, any region of the palm can be scored and the top-ranked regions are used to train the filters for each class. Using an enhanced palmprint segmentation algorithm, our proposed classifier achieves an average equal error rate of 1.12 times10-4% on a large database of 385 classes using multiple filters of size 64 times 64 pixels. The average false acceptance rate when the false rejection rate is zero is 2.25 times10-4%.

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.

Face recognition degrades when faces are of very low resolution since many details about the difference between one person and another can only be captured in images of sufficient resolution. In this work, we propose a new procedure for recognition of low-resolution faces, when there is a high-resolution training set available. Most previous super-resolution approaches are aimed at reconstruction, with recognition only as an after-thought. In contrast, in the proposed method, face features, as they would be extracted for a face recognition algorithm (e.g., eigenfaces, Fisher-faces, etc.), are included in a super-resolution method as prior information. This approach simultaneously provides measures of fit of the super-resolution result, from both reconstruction and recognition perspectives. This is different from the conventional paradigms of matching in a low-resolution domain, or, alternatively, applying a super-resolution algorithm to a low-resolution face and then classifying the super-resolution result. We show, for example, that recognition of faces of as low as 6 times 6 pixel size is considerably improved compared to matching using a super-resolution reconstruction followed by classification, and to matching with a low-resolution training set.

We present a new series of results that show the competitive performance of advanced correlation filter classifiers for palmprint recognition. We design multiple correlation filters in subregions of the palmprint for each class. We propose a segmentation stage that selects palmprint subregions to train the filters in a class-by-class basis using different edge-detection operators. This effectively guides the filter training process to rely on regions that have a stronger line content, increasing between-class separation of the palm-prints. We evaluate the proposed algorithm in a large palmprint database of 385 classes. Our preliminary results show that most classes can be perfectly separated and the average equal error rates are as low as 0.0003% for regions of interest of size 64 times 64 pixels.

This paper introduces the application of correlation filter classifiers for palmprint identification and verification. Correlation filter classifiers have been previously applied to other biometric classification tasks, but not to classification of palmprint images. We describe how the extraction of an appropriate region of interest in the palmprint surface can be used to design correlation filters that accomplish 100% recognition on a database of 50 persons.