Feature Sharing Applied to Palmprint Identification

Palmprint identification is the means of recognizing an individual from the database using his or her palmprint features. This paper presents a palmprint recognition method by finding common features that can be shared across the classes (different persons), which is a welcome contrast to the existing approaches. At present almost all research is on low resolution images for civil and commercial applications, hence we trained classifiers working in similar feature spaces of the low dimensionality such as texture. In enrollment stage, we randomly select a certain number of samples each class as the training samples. After being preprocessed, the improved SIFT (Scale Invariant Feature Transformation) feature with SAX (Symbolic Aggregate approximation) representation is extracted from the image. The ECOC (Error Correct Output Code) matrix is obtained by layer joint boosting to select the best shared models and their shared features correspondingly. The sum of the weak classifiers which are constructed in each round of boosting turns into the strong classifier. In identification stage, query sample after being extracted features can be directly identified. The experimental results illustrate the effectiveness of the proposed approach.