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Highlights • We develop a deep local descriptor for cross-modality face recognition, which can learn discriminant information from image patches. • We propose an enumeration loss function to eliminate modality gap on local patch level, which is integrated into a convolutional neural network. • Extensive experiments show that DLFace outperforms existing methods, which demonstrate the effectiveness of our method. Abstract Cross-modality face recognition aims to identify faces across different modalities, such as matching sketches with photos, low resolution face images with high resolution images, and near infrared images with visual lighting images, which is challenging because of the modality gap caused by texture, resolution, and illumination variations. Existing approaches either utilized hand-crafted approaches which ignore inherent data distribution characteristic, or applied deep learning-based algorithms on holistic face images with facial local information ignored. In this paper, we propose a deep local descriptor learning framework for cross-modality face recognition, which aims to learn discriminant and compact local information directly from raw facial patches. A novel cross-modality enumeration loss is proposed to eliminate the modality gap on local patch level, which is then integrated into a convolutional neural networks for deep local descriptor extraction. The proposed deep local descriptor can be easily applied to any traditional face recognition systems, and we use Fisherface as an example in the paper. Extensive experiments on six widely used cross-modality face recognition datasets demonstrate the superiority of proposed method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Local descriptors are widely used technique of feature extraction to obtain information about both local and global properties of an object. Here, we discuss an application of the Chain Code-Based Local Descriptor to face recognition by focusing on various datasets and considering different variants of this description method. We augment the generic form of the descriptor by adding a possibility of grouping pixels into blocks, i.e., effectively describing larger neighborhoods. The results of experiments show the efficiency of the approach. We demonstrate that the obtained results are comparable or even better than those delivered by other important algorithms in the class of methods based on the Bag-of-Visual-Words paradigm. [ABSTRACT FROM AUTHOR]

Human palmprint-based biometric solutions have been studied extensively in both controlled and uncontrolled environments. However, the majority of existing methods do not reliably handle variations of translation, rotation, and blurriness of one's palm within the range of acceptable tolerance, which largely degrades the performance. Therefore, this paper presents a unique local descriptor called Local Micro-structure Tetra Pattern (LMTrP) and its application to palmprint recognition. The proposed descriptor takes advantage of local descriptors’ direction as well as thickness. In this paper, the palmprint image is first filtered with the line-shaped filter to effectively eliminate unnecessary features. Then, local region histograms of LMTrP are extracted and concatenated into one feature vector to represent the given image. Finally, the kernel linear discriminant analysis is applied on the feature vector for dimension reduction. The experimental results indicate that the proposed methods significantly outperform the state-of-the-art methods without the need to align the palmprint images. [ABSTRACT FROM AUTHOR]

Local binary patterns (LBP) are one of the most important image representations. However, LBPs have not been as successful as other methods in palmprint recognition. Originally, the LBP descriptor methods construct feature vectors in the image intensity space, using pixel intensity differences to encode a local representation of the image. Recently, similar feature descriptors have been proposed which operate in the gradient space instead of the image intensity space, such as local directional patterns (LDP) and local directional number patterns (LDN). In this paper, we propose a new feature input space and define an LBP-like descriptor that operates in the local line-geometry space, thus proposing a new image descriptor, local line directional patterns (LLDP). Moreover, the purpose of this work is to show that different implementations of LLDP descriptors perform competitively in palmprint recognition. We evaluate variations to LLDPs, e.g., the modified finite radon transform (MFRAT) and the real part of Gabor filters are exploited to extract robust directional palmprint features. As is well-known, palm lines are the essential features of a palmprint. We are able to show that the proposed LLDP descriptors are suitable for robust palmprint recognition. Finally, we present a thorough performance comparison among different LBP-like and LLDP image descriptors. Based on experimental results, the proposed feature encoding of LLDPs using directional indexing can achieve better recognition performance than that of bit strings in the Gabor-based implementation of LLDPs. We used four databases for performance comparisons: the Hong Kong Polytechnic University Palmprint Database II, the blue band of the Hong Kong Polytechnic University Multispectral Palmprint Database, the Cross-Sensor palmprint database, and the IIT Delhi touchless palmprint database. Overall, LLDP descriptors achieve a performance that is competitive or better than other LBP descriptors. [ABSTRACT FROM AUTHOR]

Abstract: Visual content description is a key issue for machine-based image analysis and understanding. A good visual descriptor should be both discriminative and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. In this paper, we propose a new operator called the orthogonal combination of local binary patterns (denoted as OC-LBP) and six new local descriptors based on OC-LBP enhanced with color information for image region description. The aim is to increase both discriminative power and photometric invariance properties of the original LBP operator while keeping its computational efficiency. The experiments in three different applications show that the proposed descriptors outperform the popular SIFT, CS-LBP, HOG and SURF, and achieve comparable or even better performances than the state-of-the-art color SIFT descriptors. Meanwhile, the proposed descriptors provide complementary information to color SIFT, because a fusion of these two kinds of descriptors is found to perform clearly better than either of the two separately. Moreover, the proposed descriptors are about four times faster to compute than color SIFT. [Copyright &y& Elsevier]

Abstract: A new approach, based on the hierarchical soft correspondence detection, has been presented for significantly improving the speed of our previous HAMMER image registration algorithm. Currently, HAMMER takes a relative long time, e.g., up to 80min, to register two regular sized images using Linux machine (with 2.40GHz CPU and 2-Gbyte memory). This is because the results of correspondence detection, used to guide the image warping, can be ambiguous in complex structures and thus the image warping has to be conservative and accordingly takes long time to complete. In this paper, a hierarchical soft correspondence detection technique has been employed to detect correspondences more robustly, thereby allowing the image warping to be completed straightforwardly and fast. By incorporating this hierarchical soft correspondence detection technique into the HAMMER registration framework, both the robustness and the accuracy of registration (in terms of low average registration error) can be achieved. Experimental results on real and simulated data show that the new registration algorithm, based on the hierarchical soft correspondence detection, can run nine times faster than HAMMER while keeping the similar registration accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new invariant local descriptor, called the contrast context histogram (CCH), for image matching and object recognition. By representing the contrast distributions of a local region, it serves as a distinctive local descriptor of the region. Our experiments demonstrate that contrast-based local descriptors can represent local regions with more compact histogram bins. Because of its high matching accuracy and efficient computation, the CCH has the potential to be used in a number of real-time applications. [Copyright &y& Elsevier]