Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices. The static images naturally lose the dynamic characteristics of retina fluctuation, resulting in diminished dataset richness, and the usage of bench-top devices further restricts dataset scalability due to its limited accessibility. Considering these limitations, we introduce the first video-based retinal dataset by employing handheld devices for data acquisition. The dataset comprises 635 smartphone-based fundus videos collected from four different clinics, involving 415 patients from 50 to 75 years old. It delivers comprehensive and precise annotations of retinal structures in both spatial and temporal dimensions, aiming to advance the landscape of vasculature segmentation. Specifically, the dataset provides three levels of spatial annotations: binary vessel masks for overall retinal structure delineation, general vein-artery masks for distinguishing the vein and artery, and fine-grained vein-artery masks for further characterizing the granularities of each artery and vein. In addition, the dataset offers temporal annotations that capture the vessel pulsation characteristics, assisting in detecting ocular diseases that require fine-grained recognition of hemodynamic fluctuation. In application, our dataset exhibits a significant domain shift with respect to data captured by bench-top devices, thus posing great challenges to existing methods. Thanks to rich annotations and data scales, our dataset potentially paves the path for more advanced retinal analysis and accurate disease diagnosis. In the experiments, we provide evaluation metrics and benchmark results on our dataset, reflecting both the potential and challenges it offers for vessel segmentation tasks. We hope this challenging dataset would significantly contribute to the development of eye disease diagnosis and early prevention.

Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional derivatives in a cylinder neighborhood within dynamic textures. Unlike traditional methods, a nonbinary texture coding technique is proposed to extract high-order derivatives using continuous circular and cylinder regions to avoid aliasing effects. Then, these local linear subspaces of texture segments are mapped onto a Grassmann manifold via sparse representation. A new joint sparse representation algorithm is developed to establish the correspondences of subspace points on the manifold for measuring the similarity between two dynamic textures. Extensive experiments on the Honda/UCSD, the CMU motion of body, the YouTube, and the DynTex datasets show that the proposed method consistently outperforms the state-of-the-art methods in dynamic texture recognition, and achieved the encouraging highest accuracy reported to date on the challenging YouTube face dataset. The encouraging experimental results show the effectiveness of the proposed method in video-based face recognition in human-machine system applications.

This paper presents a novel feature, Topographic High-order Derivatives (THD) for 3D face recognition. THD is based on the high-order micro-pattern information extracted from face topography maps. Face topography maps are partitioned into polar sectors, and THDs are computed using directional highorder derivatives within the sectors. Local features are extracted by encoding directional high-order derivatives within polar neighborhoods. To evaluate the proposed method, we use Bosphorus and FRGC 3D face databases which include pose and expression changes. The performance of the proposed method is higher compared to the state-of-the-art benchmark approaches in 3D face recognition.

In recent years, 3D face recognition has become a popular solution to deal with the problem of pose-invariant face recognition. The majority of 3D face data are, however, actually 2.5D which are sensitive to pose variations. This paper presents a novel Geodesic Texture Warping (GTW) solution for 2.5D pose-invariant face recognition. In this method, we use the geodesic distance computed on a 2.5D face scan to warp the texture of a rotated face to that of a frontal one to perform matching. A feasibility and effectiveness investigation for the proposed method is conducted using a wide range of experiments including samples with different face rotations. The encouraging experimental results demonstrate that the proposed method achieves much higher accuracy than the state-of-the-art method with a low computational cost.

This paper introduces an efficient method for face localization and recognition in color images. The proposed method uses the location of eyes for computation and extraction of a face's bounding ellipse. In this way, parameters of a face's ellipse (center, orientation, major and minor axis), is computed by the location of eyes in a face image. In the next step, we apply Pseudo Zernike Moments (PZM), Zernike Moments (ZM) and Principal Component Analysis (PCA) for feature extraction. For classification of these feature vectors a new structure of RBF neural networks with a novel distance function is introduced and a new method for determination of RBF unit parameters is proposed. Finally, we compare the efficiency of the proposed system for three types of feature vectors (PZM, ZM and PCA). Results emphasize the high accuracy and efficiency of the PZM features proportion to other features (ZM and PCA) for use in the proposed recognition system.

In this paper a fuzzy approach for the classification of skin color tones in color images is presented. The paper is divided into two stages. The first stage consists of the selection of images that contained human faces of different skin color tones. A subset made up of these images was submitted to the opinion of a group of people with the aim of classifying them into their respective skin color tones: Black, Brown, and White. In the second stage of the paper, the information obtained from the research carried out, jointly with the study of the colors and their defining tones in relation to the RGB color system, were used for the definition of the fuzzy sets as well as the inference rules implemented into the system. In this manner, the developed system is able to classify a determined color into a possible skin color.

This paper presents a face detection framework based on up to the third-order two-dimensional central geometrical moments (CGMs) of face components and their horizontal and vertical gradients. To detect faces in an image an exhaustive search over space and scale is carried out by using a multistage classifier which quickly discards background regions and spends more computation on promising face-like regions. A new method for fast computation of up to the third-order local geometrical moments, suitable for sliding window applications is presented whose computational complexity is invariant to scale and is much faster compared to previous methods for PC-based applications. The presented results show that the proposed system yields good performance in terms of detection and false positive rates.