Multi-angle Face Research Articles

Multi-angle face expression recognition based on integration of lightweight deep network and key point feature positioning

In the current advancement of science and technology, robot and machine vision technology have significant application value. Due to their mimetic structure, quadruped robots are better equipped to adapt to the requirements of walking over complex terrain. This paper introduce lightweight deep network and combining key point feature positioning for multi-angle face expression recognition. Using robot dog to recognize facial expressions will be affected by distance and angle. To solve this problem, this paper proposes a method for facial expression recognition at different distances and angles,which solved the larger distance and deflection angle of face expression recognition accuracy and real-time issues. The dataset used in this study was collected by adjusting the camera mounted in an AI robotic dog in a laboratory scenario. The result shows a common and fast and intuitive visual representation of human beings to convey emotions or mental states, facial expression is an effective way for robots to obtain information and interact with people through visual images.

Research on Multi-Angle Face Detection Method Based on Improved YOLOV2 Algorithm

Face detection based on the YOLOV2 algorithm can achieve a higher level of accuracy and show a faster detection speed. Therefore, the YOLOV2 algorithm appears more frequently in the field of target real-time detection. The traditional YOLOV2 algorithm uses the Darknet-19 network. Based on this, this paper builds a new network by merging the residual network (ResNet). In addition, in order to further make up for the defects exposed by the YOLOV2 algorithm in face detection, we change the weight of the loss function and implement an image enhancement scheme on the training set. The YOLOV2 algorithm and the improved YOLOV2 algorithm are compared and tested on the Wider Face dataset, and then we use face data sets from different angles to test the performance of the algorithm. According to the analysis of the experimental results, the improved YOLOV2 algorithm can show higher accuracy and robustness in multi-angle face detection.

Residual mining caving face over the roadway and the key technology of deformation

Multi layer multi angle face roadway of coal mining face of residual mining brings technical challenges to the safety of the working face mining. With the background of the 6212 residual coal mining face in a mine in Shanxi, the deformation laws of the surrounding rock in the slanted roadway, the bottom coal slanting roadway and the intersection point are analyzed by numerical simulation. The simulation results show that at least the reinforcement measures should be carried out by 20m in the empty lane, and the plastic zone in the floor of the roadway along the top is obviously developed, and the stress concentration of the surrounding rock is high, and the support and management should be strengthened. In view of the space position of the site, the key technical measures are put forward, such as dense support or wood stack, anchor net cable, and super high water material grouting pre-filling, so as to ensure the safe recovery of the residual mining face.

Multi-angle face detection based on DP-Adaboost

Although important progresses have been already made in face detection, many false faces can be found in detection results and false detection rate is influenced by some factors, such as rotation and tilt of human face, complicated background, illumination, scale, cloak and hairstyle. This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate. An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face. An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm. Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier, the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.

A Multi-angle Face Recognition Algorithm Based on Modified Gaussian Process Latent Variable Mode

A Multi-angle Face Recognition Algorithm Based on Modified Gaussian Process Latent Variable Mode

基于多个再生核希尔伯特空间的多角度人脸识别

The traditional spectrum algorithms are limited in face recognition problem.For its characteristics of problem,a novel method based on multi-reproducing Kernel Hilbert space was proposed.Firstly,the images were processed by the landmark method,and the angle of training images could be obtained.Secondly,the face data was iterated by the Kernel,then face data expressed linearly in the reproducing Kernel Hilbert space.Thereafter,for many types of face data,the multi-reproducing Kernel Hilbert space were established.Finally,the reproducing Kernel Hilbert space belonging of human face image was judged by the comparison of training images,and the multi-angle face recognition achieved.The two classes of data sets were selected as the experimental data,which consisted of FERET and CMU-PIE.A large number of experiments were carried out.The results show that the proposed method has great effect to recognise multi-angle face.The average recognition rate and efficiency are 5% and 20% higher than the traditional algorithms,respectively.

Multi-Angle Face Detection Using Back Propagation Neural Network

In machine vision application, the main part to analyze an image is to identify its features which contribute to efficiency of the system. Many applications in vision system and image analysis used face detection as a feature of their whole system development. In application such as video surveillance, fatigue detection and security system, face is a fundamental step in the analysis before proceed to system implementation. It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. In this paper, we propose a neural network based approach to identify multi-angle face which falls into five categories: all left-side face, half left-side face, positive face, half right-side face, and all right-side face. More than 100 images of each category have been used for training and testing of face detection and its features was extracted to be an input to BP neural network. We analyzed the result of training and testing set of neural network and the best classification achieved was 90.7%.