3D Palmprint Identification Using Block-Wise Features and Collaborative Representation

Abstract

Abstract-Developing 3D palmprint recognition systems has recently begun to draw attention of researchers. Compared with its 2D counterpart, 3D palmprint has several unique merits. However, most of the existing 3D palmprint matching methods are designed for one-to-one verification and they are not efficient to cope with the one-to-many identification case. In this paper, we fill this gap by proposing a collaborative representation (CR) based framework with l 1 -norm or l 2 -norm regularizations for 3D palmprint identification. The effects of different regularization terms have been evaluated in experiments. To use the CR-based classification framework, one key issue is how to extract feature vectors. To this end, we propose a block-wise statistics based feature extraction scheme. We divide a 3D palmprint ROI into uniform blocks and extract a histogram of surface types from each block; histograms from all blocks are then concatenated to form a feature vector. Such feature vectors are highly discriminative and are robust to mere misalignment. Experiments demonstrate that the proposed CR-based framework with an l 2 -norm regularization term can achieve much better recognition accuracy than the other methods. More importantly, its computational complexity is extremely low, making it quite suitable for the large-scale identification application. Source codes are available at

Multi‐Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification

“…Quite recently, Zhang et al [38] claimed that all the abovementioned methods are only appropriate for one-to-one verification applications and they are not suitable for large-scale one-to-many identification applications. The main reason is that they all adopt a brute-force searching strategy for identification.…”

“…Moreover, for dealing with the mere misalignment between two ROIs, they used the multi-translation-based matching [36,55] or explicit registration techniques [54,58], both of which are not quite computationally efficient. As a solution, Zhang et al [38] proposed a new scheme for 3D palmprint identification, namely, CR L2, which makes use of CRC RLS [42] as the classification framework. Additionally, to represent a 3D palmprint sample, they proposed a patchwise and statistics-based feature extraction scheme, having the merits of high effectiveness and high robustness to mere misalignment.…”

“…Owing to the fact that both SRC [6] and CRC RLS [42] are based on the collaborative representation, they are referred to as CR-based classification frameworks in this paper. Having investigated the literature, we find that CR-based approaches can get the state-of-the-art results for 3D ear or palmprint identification [35,38]. In existing CR-based 3D ear or palmprint classification approaches, to represent a range image sample, a single feature vector is extracted, which is actually a type of holistic representation.…”

“…Since mere misalignment between two ROIs exists, it is highly desired that the extracted feature vectors are robust to mere misalignment error while possessing a high discriminating power. To satisfy aforementioned needs, Zhang et al [38] proposed a patch-wise and statistics-based feature extraction scheme, which makes use of local surface type information of the range image. In this paper, we extend Zhang et al A range image can be regarded as a 3D surface, containing various convex and concave local structures.…”

“…Then, based on (the surface type map) and (the CompCode map), we resort to the patch-wise and statisticsbased scheme [38] to build the feature vector. Specifically, we uniformly partition ( ) into a set of regular patches.…”