Facial Geometric Feature for Cascade Eye Detection

In this paper, we propose an eye detection method that is robust to facial pose changes using gradient directional features of brightness and a particle filter. The rejection function of incorrect eye detection in the proposed method allows for the eye detection again even if the eye is incorrectly detected. In this method, to estimate the boundary between the iris and sclera or eyelid, the gradient intensities are calculated by four directional Prewitt filters in four regions. The likelihood used in the particle filter is obtained by averaging the gradient intensities for the specific direction in the four regions and the upper eyelid area. From experimental results, the average detection rates of both eyes for roll, yaw, and pitch angles of the face are more than 90% by using rejection function for incorrect eye detection. The rejection function produces the 4.4%, 4.5%, and 4.9% increases in average detection rates of both eyes for roll, yaw, and pitch facial angles, respectively. The proposed eye detection method can track both eye in real-time (about 20 ms) and is robust to the facial pose changes.

In variable-bit-rate video coding, the video is preprocessed to collect sequence-level statistics, which are used for global bit allocation in the actual encoding stage to obtain a smoothed video presentation quality. However, in real-time video recording and network streaming, this type of two-pass encoding scheme is not allowed because the access to future frames and global statistics is not available. To address this issue, we introduce the concept of low-pass filtering of rate-distortion functions and develop a smoothed rate control (SRC) framework for real-time video recording and streaming. Theoretically, we prove that, using a geometric averaging filter, the SRC algorithm is able to maintain a smoothed video presentation quality while achieving the target bit rate automatically. We also analyze the buffer requirement of the SRC algorithm in real-time video streaming, and propose a scheme to seamlessly integrate robust buffer control into the SRC framework. The proposed SRC algorithm has very low computational complexity and implementation cost. Our extensive experimental results demonstrate that the SRC algorithm significantly reduces the picture quality variation in the encoded video clips.

Eye detection is the most important and critical task of diverse applications such as face detection and recognition. However, most eye detection methods do not fully consider detection robustness to people with glasses, illumination variation, head pose change, and eye occlusions. This paper proposes an efficient hybrid eye detection method based on a gray intensity variance filter (VF) and support vector machines (SVMs). Firstly, the VF is used for eliminating most of noneye region images to keep less candidate eye regions. Then accurate two eye regions are determined easily through the trained SVM classifier. Moreover, this paper provides an assessment of the sensitivity of obtained parameters in the SVM classifier on eye detection accuracy. The proposed method was evaluated on different face databases. The experimental results show that the method can improve the performance of eye detection and achieve a higher detection accuracy compared with state-of-the-art methods.

Facial expression description and recognition based on fuzzy semantic concepts

Focused on the issue that the detection error rate of the current eye detection method is relatively high, and when the AdaBoost algorithm is used to train the classifier, it is easy to appear the phenomenon of weight imbalance. A new eye detection method based on the improved AdaBoost algorithm is proposed. First, the AdaBoost algorithm is applied to the detection of human eyes. Then the reason for the imbalance of weights in training of AdaBoost algorithm is analyzed, and the concept of missing detection rate is introduced to improve the weight updating process of AdaBoost algorithm. The experimental results show that the improved AdaBoost algorithm ensured the sample weight distribution balance and improve the accuracy in the training process; eye detection based on the improved AdaBoost algorithm effectively maintains a high detection rate and inhibits the detection error rate, makes detection more accurate.

Remote laboratories are an inevitable necessity for Internet enabled education in Science, Technology, Engineering, and Math (STEM) fields due to their effectiveness, flexibility, cost savings, and the fact that they provide many benefits to students, instructors and researchers. Accordingly, real-time experiment live video streaming is an essential part of remote experimentation operation. Nevertheless, in the development of real-time experiment video transmission, it is a key and difficult issue that the video is transferred across the network firewall in most of the current remote laboratory solutions. To address the network firewall issue, we developed a complete novel solution via HTTP Live Streaming (HLS) protocol and FFMPEG that is a powerful cross-platform command line video trans-code/encoding software package on the server side. In this paper, a novel, real-time video streaming transmission approach based on HLS for the remote laboratory development is presented. With this new solution, the terminal users can view the real-time experiment live video streaming on any portable device without any firewall issues or the need for a third party plug-in., This new solution also significantly benefits remote laboratory development in the future.

Face and eye detection is one of the most challenging problems in computer vision area. The goal of this paper is to present a study over the existing literature on face and eye detection and gaze estimation. With the uptrend of systems based on face and eye detection in many different areas of life in recent years, this subject has gained much more attention by academic and industrial area. Many different studies have been performed about face and eye detection. Besides having many challenging problems like, having different lighting conditions, having glasses, facial hair or mustache on face, different orientation pose or occlusion of face, face and eye detection methods performed great progress. In this paper we first categorize face detection models and examine the basic algorithms for face detection. Then we present methods for eye detection and gaze estimation.

Detection and tracking of eyes for gaze-camera control

Recent advances in mobile technologies have enabled a plethora of new applications. The hardware capabilities of mobile devices, however, are still insufficient for real-time stream data processing (e.g., real-time video stream). In order to process real-time streaming data, most existing applications offload the data and computation to a remote cloud service, such as Apache Storm or Apache Spark Streaming. Offloading streaming data, however, has high costs for users, e.g., significant service fees and battery consumption. To address these challenges, we design, implement and evaluate Mobile Storm, the first stream processing platform for mobile clouds, leveraging clusters of local mobile devices to process real-time stream data. In Mobile Storm, we model the workflow of a real-time stream processing job and decompose it into several tasks so that the job can be executed concurrently and in a distributed manner on multiple mobile devices. Mobile Storm was implemented on Android phones and evaluated extensively through a real-time HD video processing application. The result shows that Mobile Storm effectively processes HD Video Stream in a mobile cloud, which would be impossible on a single mobile device.

Target detection and tracking in real-time video streaming is one of the core tasks in the field of computer vision, and its performance directly affects the effectiveness of intelligent surveillance, automatic driving and other applications. In this paper, a three-frame frame difference method incorporating Sobel operator is proposed to optimize the boundary extraction by combining morphological filtering. Meanwhile, the Mean Shift-based target tracking algorithm is improved by introducing a recognizable interferer model with a dual attention mechanism to enhance the tracking robustness. Experiments show that the improved inter-frame difference method achieves F-measure of 93.38%, 96.20% and 91.83% on Highway, Road and Highway2 datasets, respectively, and the detection frame rate is improved to 32 f/s. The improved Mean Shift algorithm improves the tracking accuracy by 2.67%-3.09% and the success rate by 0.98%-1.75% over the mainstream algorithm on the UAV123 and VOT2024 datasets. In addition, the multi-target tracking experiments achieve MOTA of 75.76% and 80.61% on the MOT16 and MOT17 datasets, respectively, validating the comprehensive performance advantages of the algorithms. This study provides an efficient and robust solution for target detection and tracking in real-time video streaming.

In refractive eye surgery using laser, automatically localizing a target eye for surgery and precisely focusing the laser to the corneal surface are crucial to the medical treatment. We therefore propose a new eye detection method based on template matching for automatic localizations of target eyes for refractive eye surgery with excimer laser. In the proposed approach, representative templates of image gradient histograms of eyes are constructed using both open and privately-collected eye datasets. Real-time eye detection that helps guiding the excimer laser to a target eye for surgery can be achieved by the efficient template matching. After the eye localization, image-guided, automatic alignment of positioning marker, i.e., a read line of visible light, is then employed to measure the distance between the corneal surface and the excimer laser. Experimental results show that the proposed eye detection method using template matching outperforms an HoG-based eye detector in miss detection rates based on the same training data for this specific medical application. Also, with the automatic image-guided positioning of laser head, the time consuming process of manual laser focusing can be largely simplified.

This paper presents the evaluation of visual features for the proposed two eye detection method applied to thermal images. The use of two eye region is due to its distinctive pattern and to overcome the issue of blurred and noisy characteristic in the thermal image. Comparative performance analysis on three different features which includes Haar, Histogram of Oriented Gradients (HoG) and Local Binary Patterns (LBP) is conducted. The performance of the eyes detection method is measured based on the correct detection of both eyes inside the face image. The experiments were done on the Natural Visible and Infrared Facial Expression Database (NVIE). The method proposed in this paper shows good eye detection accuracy. The best detection accuracy is obtained using the HoG feature.

Eye detection can be used in intelligent human-computer interfaces, driver drowsiness detection, security, and biology systems. In this paper a new method for eye detection based on rectangle features and pixel-pattern-based texture feature (PPBTF) is proposed. First, Adaboost cascade classifier by rectangle features is constructed to do rough eye detection in a front facial image. Second, the result image patches are cropped and scaled to 24×12 to compute the features of PPBTF, then, put these features into an Adaboost and SVM classifier for an accurate detection. Some eye open front facial images from FERET Database are chosen to do the experiments, and the results show that the approach is real time and is effective for eye detection.

Image based eye detection and gaze estimation have a wide range of potential applications, such as medical treatment, biometrics recognition, human-computer interaction. Though a large number of researchers have attempted to solve the two problems, they still exist some challenges due to the variation in appearance and lack of annotated images. In addition, most related work perform eye detection first, followed by gaze estimation via appearance learning. In this paper, we propose a unified framework to execute the gaze estimation and the eye detection simultaneously by learning the cascade regression models from appearance around the eye related key points. Intuitively, there is coupled relationship among location of eye center, shape of eye related key points, appearance representation and gaze information. To incorporate these information, at each cascade level, we first learn a model to map the shape and appearance around current eye related key points to the three dimension gaze update. Then, with the help of estimated gaze, we further learn a regression model to map the gaze, shape and appearance information to eye location update. By leveraging the power of cascade learning, the proposed method can alternatively optimize the two tasks of eye detection and gaze estimation. The experiments are conducted on benchmarks of GI4E and MPIIGaze. Experimental results show that our proposed method can achieve preferable results in gaze estimation and outperform the state-of-the-art methods in eye detection.

5G mobile communication infrastructure attracts increasing attention of stakeholders, going beyond only interconnection of people and increasingly serve to connect and manage 5G-enabled IoT devices. Among others, unmanned aerial vehicles (UAVs) or systems (UAS) already rely on 5G communication infrastructure and in the near future are envisioned to use it even more. 5G systems already support a wide-variety of different applications, such as remote healthcare, self-driving ground vehicles, virtual or augmented reality, drones, surveillance and many more. Among these is the high-resolution video surveillance using drones for different purposes. In road traffic analysis, one of the most important public safety applications, the low compressed or uncompressed video stream can greatly improve the analysis and traffic incident detection performance. On the other hand it is challenging to transmit high-resolution video stream in real time due to its data size. This paper provides an overview of the current research in the area of UAV command and control using 5G systems describing basic concepts and challenges. We review some of the latest research in regard to real-time high-resolution video transfer. A brief discussion of experiments on the 5G private campus network communication between a drone and a ground analytical system is presented.