Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc. Although numerous of efforts have already been paid, most of them focus on addressing the eyeblink detection problem under the constrained indoor conditions with the relative consistent subject and environment setup. Nevertheless, towards the practical applications eyeblink detection in the wild is more required, and of greater challenges. However, to our knowledge this has not been well studied before. In this paper, we shed the light to this research topic. A labelled eyeblink in the wild dataset (i.e., HUST-LEBW) of 673 eyeblink video samples (i.e., 381 positives, and 292 negatives) is first established by us. These samples are captured from the unconstrained movies, with the dramatic variation on human attribute, human pose, illumination condition, imaging configuration, etc. Then, we formulate eyeblink detection task as a spatial-temporal pattern recognition problem. After locating and tracking human eye using SeetaFace engine and KCF tracker respectively, a modified LSTM model able to capture the multi-scale temporal information is proposed to execute eyeblink verification. A feature extraction approach that reveals appearance and motion characteristics simultaneously is also proposed. The experiments on HUST-LEBW reveal the superiority and efficiency of our approach. It also verifies that, the existing eyeblink detection methods cannot achieve satisfactory performance in the wild.
Accurate eye localization is the key step for eyeblink detection within spatial domain. Some existing approaches @cite_14 @cite_2 @cite_21 resort to using color or spectral characteristics to locate eye. Another way is to use motion information @cite_35 to detect and track eye. Nevertheless, their performance is not promising. Most of the state-of-the-art methods @cite_30 @cite_16 @cite_19 @cite_43 @cite_24 resort to detect facial landmark to this end in the way of face parsing. To achieve the balance between effectiveness and efficiency, we choose use SeetaFace engine @cite_26 for eye detection first, and then track eye using KCF @cite_22 for high efficiency.
@cite_30 @cite_35 @cite_14 @cite_26 @cite_22 @cite_21 @cite_24 @cite_19 @cite_43 @cite_2 @cite_16
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An efficient optic disk localization and segmentation are important tasks in an automated retinal image analysis system. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents a method to automatically locate and boundary detect of the optic disk. The detection procedure comprises two independent methodologies. On one hand, a location methodology obtains a pixel that belongs to the OD using iterative thresholding method followed by Principal Component Analysis techniques (PCA) and, on the other hand, a boundary segmentation methodology estimates the OD boundary by applying region-based active contour model in a variational level set formulation (RSF). The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity The results from the RSF method were compared with conventional optic disk detection using a geometric active contour models (ACM) and later verified with hand-drawn ground truth. Results indicate 89 accuracy for identification and 95.05 average accuracy in localizing the optic disc boundary. The localization of eye centers and tracking of gaze constitutes an integral component of many human-computer interaction applications. A number of constraints including intrusiveness, mobility, robustness and high-price of eye tracking systems have hindered the way of the integration of eye trackers in everyday applications. Several 'passive' systems based on a single camera have been lately proposed in the literature, exhibiting though subordinate precision compared to the commercial, hardware-based eye tracking devices. In this paper we introduce an automatic, non-intrusive method for precise eye center localization in low resolution images, acquired from single low-cost cameras. To this end, the proposed system uses color information to derive a novel eye map that emphasizes the iris area and a radial symmetry transform which operates both on the original eye images and the eye map. The performance of the proposed method is extensively evaluated on four publicly available databases containing low resolution images and videos. Experimental results demonstrate great accuracy in challenging cases and resilience to pose and illumination variations, achieving significant improvement over existing methods. Use of color information for accurately localizing eye centers.Statistical analysis of the color distribution of the skin, eye areas and irises.Radial symmetry computation both in grayscale images and in chrominance eye maps.We report high accuracy rates for low resolution color images. A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers. In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50 ) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results.