Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.
Signature authentication systems often have to focus their processing on acquired dynamic and/or static signatures descriptors to authenticate persons. This approach gives satisfactory results in ordinary cases but remains vulnerable against skilled forgeries. This is mainly because there is no relation between the signatory and his signature. We will show that the inclusion of the hand shape in the authentication process will considerably reduce the false acceptance rates of skilled forgeries and improve the authentication accuracy performances. A new online hand signature authentication approach based on both signature and hand shape descriptor is proposed. The signature acquisition is completely transparent, which allows a high level of security against fraudulent imitation attempts. Authentication performances are evaluated with extensive experiments. The obtained test results [equal error rate (EER)=2%, genuine acceptance rate (GAR)=96%]confirm the efficiency of the proposed approach.
We propose a unified approach to propagate knowledge into a high-dimensional space from a small informative set, in this case, scale invariant feature transform (SIFT) features. Our contribution lies in three aspects. First, we propose a spectral graph embedding of the SIFT points for dimensionality reduction, which provides efficient keypoints transcription into a Euclidean manifold. We use iterative deflation to speed up the eigendecomposition of the underlying Laplacian matrix of the embedded graph. Then, we describe a variational framework for manifold denoising based on p -Laplacian to enhance keypoints classification, thereby lessening the negative impact of outliers onto our variational shape framework and achieving higher classification accuracy through agglomerative categorization. Finally, we describe our algorithm for multilabel diffusion on graph. Theoretical analysis of the algorithm is developed along with the corresponding connections with other methods. Tests have been conducted on a collection of images from the Berkeley database. Performance evaluation results show that our framework allows us to efficiently propagate the prior knowledge.
We present a nonconstraining and low-cost online signature acquisition system that has been developed to enhance the performances of an existing multimodal biometric authentication system (based initially on both voice and image modalities). A laboratory prototype has been developed and validated for an online signature acquisition.
Retrieving images form large collections using image content is an important problem, in this multimedia age. A quick content-based visual access to the stored image is capital for efficient navigation through image collections. In this paper we introduce several techniques which characterize color homogeneous object and their spatial relationships for efficient content-based image retrieval. We present a region growing technique for efficient color homogeneous objects segmentation and extend the 2D string to an accurate description of spatial information and relationships. In order to improve content-based image retrieval, our method emphasized several objectives, such as: automated extraction of localize coherent regions and visual features, development of techniques for fast indexing and retrieval, and querying by both features and spatial information coupled with a symbolic level of image representation. We present our flexible image retrieval system and we give some experimental results.
Indexing is an important aspect of video database management. Video indexing involves the analysis of video sequences, which is a computationally intensive process. However, effective management of digital video requires robust indexing techniques. The main purpose of our proposed video segmentation is twofold. Firstly, we develop an algorithm that identifies camera shot boundary. The approach is based on the use of combination of color histograms and block-based technique. Next, each temporal segment is represented by a color reference frame which specifies the shot similarities and which is used in the constitution of scenes. Experimental results using a variety of videos selected in the corpus of the French Audiovisual National Institute are presented to demonstrate the effectiveness of performing shot detection, the content characterization of shots and the scene constitution.
Currently the most content-based retrieval methods of images are based on global features like histograms. Few methods have considered the spatial information for the indexing and query purpose. In this paper we present an efficient multi-dimensional spatial indexing method based on the Peano key ordering of spatial locality of regions. The Peano order gives a direct mapping between an integer and its corresponding element in the multi-dimensional space. The position in the ordering of each region in an image can be simply determined by interleaving the bits of the x and y coordinates of the region. In our method, global features of the query image like histograms of colors are first used to eliminate images in the database, which are not similar. Then the query is decomposed into a quadtree in order to extract characteristics, for instance predominant colors, associated with each square. These spatial information are identified by a list of Peano keys. This list constitutes a spatial signature of the query image. This spatial signature is researched into candidate images. For a given candidate image, each Peano key of the signature precisely indicates the spatial region whose characteristics are compared to the ones associated with the Peano key. The main advantages of our method are twofold: first its generality since it allows to associate spatial information to every kind characteristics of images, second its efficiency because there is no need to pre- extract characteristics from images in the database.
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